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['Volker Fischer', 'Mummadi Chaithanya Kumar', 'Jan Hendrik Metzen', 'Thomas Brox']

1703.01101v1

Machine learning methods in general and Deep Neural Networks in particular have shown to be vulnerable to adversarial perturbations. So far this phenomenon has mainly been studied in the context of whole-image classification. In this contribution, we analyse how adversarial perturbations can affect the task of semantic segmentation. We show how existing adversarial attackers can be transferred to this task and that it is possible to create imperceptible adversarial perturbations that lead a deep network to misclassify almost all pixels of a chosen class while leaving network prediction nearly unchanged outside this class.

Adversarial Examples for Semantic Image Segmentation

http://arxiv.org/pdf/1703.01101v1

Title Adversarial Examples Semantic Image Segmentation Summary Machine learning method general Deep Neural Networks particular shown vulnerable adversarial perturbation far phenomenon mainly studied context wholeimage classification contribution analyse adversarial perturbation affect task semantic segmentation show existing adversarial attacker transferred task possible create imperceptible adversarial perturbation lead deep network misclassify almost pixel chosen class leaving network prediction nearly unchanged outside class Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wieland Brendel', 'Jonas Rauber', 'Matthias Bethge']

1712.04248v2

Many machine learning algorithms are vulnerable to almost imperceptible perturbations of their inputs. So far it was unclear how much risk adversarial perturbations carry for the safety of real-world machine learning applications because most methods used to generate such perturbations rely either on detailed model information (gradient-based attacks) or on confidence scores such as class probabilities (score-based attacks), neither of which are available in most real-world scenarios. In many such cases one currently needs to retreat to transfer-based attacks which rely on cumbersome substitute models, need access to the training data and can be defended against. Here we emphasise the importance of attacks which solely rely on the final model decision. Such decision-based attacks are (1) applicable to real-world black-box models such as autonomous cars, (2) need less knowledge and are easier to apply than transfer-based attacks and (3) are more robust to simple defences than gradient- or score-based attacks. Previous attacks in this category were limited to simple models or simple datasets. Here we introduce the Boundary Attack, a decision-based attack that starts from a large adversarial perturbation and then seeks to reduce the perturbation while staying adversarial. The attack is conceptually simple, requires close to no hyperparameter tuning, does not rely on substitute models and is competitive with the best gradient-based attacks in standard computer vision tasks like ImageNet. We apply the attack on two black-box algorithms from Clarifai.com. The Boundary Attack in particular and the class of decision-based attacks in general open new avenues to study the robustness of machine learning models and raise new questions regarding the safety of deployed machine learning systems. An implementation of the attack is available as part of Foolbox at https://github.com/bethgelab/foolbox .

Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

http://arxiv.org/pdf/1712.04248v2

Title DecisionBased Adversarial Attacks Reliable Attacks BlackBox Machine Learning Models Summary Many machine learning algorithm vulnerable almost imperceptible perturbation input far unclear much risk adversarial perturbation carry safety realworld machine learning application method used generate perturbation rely either detailed model information gradientbased attack confidence score class probability scorebased attack neither available realworld scenario many case one currently need retreat transferbased attack rely cumbersome substitute model need access training data defended emphasise importance attack solely rely final model decision decisionbased attack 1 applicable realworld blackbox model autonomous car 2 need le knowledge easier apply transferbased attack 3 robust simple defence gradient scorebased attack Previous attack category limited simple model simple datasets introduce Boundary Attack decisionbased attack start large adversarial perturbation seek reduce perturbation staying adversarial attack conceptually simple requires close hyperparameter tuning rely substitute model competitive best gradientbased attack standard computer vision task like ImageNet apply attack two blackbox algorithm Clarifaicom Boundary Attack particular class decisionbased attack general open new avenue study robustness machine learning model raise new question regarding safety deployed machine learning system implementation attack available part Foolbox httpsgithubcombethgelabfoolbox Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Abien Fred Agarap', 'Francis John Hill Pepito']

1801.00318v1

Effective and efficient mitigation of malware is a long-time endeavor in the information security community. The development of an anti-malware system that can counteract an unknown malware is a prolific activity that may benefit several sectors. We envision an intelligent anti-malware system that utilizes the power of deep learning (DL) models. Using such models would enable the detection of newly-released malware through mathematical generalization. That is, finding the relationship between a given malware $x$ and its corresponding malware family $y$, $f: x \mapsto y$. To accomplish this feat, we used the Malimg dataset (Nataraj et al., 2011) which consists of malware images that were processed from malware binaries, and then we trained the following DL models 1 to classify each malware family: CNN-SVM (Tang, 2013), GRU-SVM (Agarap, 2017), and MLP-SVM. Empirical evidence has shown that the GRU-SVM stands out among the DL models with a predictive accuracy of ~84.92%. This stands to reason for the mentioned model had the relatively most sophisticated architecture design among the presented models. The exploration of an even more optimal DL-SVM model is the next stage towards the engineering of an intelligent anti-malware system.

Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification

http://arxiv.org/pdf/1801.00318v1

Title Towards Building Intelligent AntiMalware System Deep Learning Approach using Support Vector Machine SVM Malware Classification Summary Effective efficient mitigation malware longtime endeavor information security community development antimalware system counteract unknown malware prolific activity may benefit several sector envision intelligent antimalware system utilizes power deep learning DL model Using model would enable detection newlyreleased malware mathematical generalization finding relationship given malware x corresponding malware family f x mapsto accomplish feat used Malimg dataset Nataraj et al 2011 consists malware image processed malware binary trained following DL model 1 classify malware family CNNSVM Tang 2013 GRUSVM Agarap 2017 MLPSVM Empirical evidence shown GRUSVM stand among DL model predictive accuracy 8492 stand reason mentioned model relatively sophisticated architecture design among presented model exploration even optimal DLSVM model next stage towards engineering intelligent antimalware system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Despoina Christou']

1604.01272v1

Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it implies the transformation of the data from a sparse high-dimensional space, to higher level meaningful abstractions. This dissertation employs Neural Networks for distributed paragraph representations, and Latent Dirichlet Allocation to capture higher level features of paragraph vectors. Although Neural Networks for distributed paragraph representations are considered the state of the art for extracting paragraph vectors, we show that a quick topic analysis model such as Latent Dirichlet Allocation can provide meaningful features too. We evaluate the two methods on the CMU Movie Summary Corpus, a collection of 25,203 movie plot summaries extracted from Wikipedia. Finally, for both approaches, we use K-Nearest Neighbors to discover similar movies, and plot the projected representations using T-Distributed Stochastic Neighbor Embedding to depict the context similarities. These similarities, expressed as movie distances, can be used for movies recommendation. The recommended movies of this approach are compared with the recommended movies from IMDB, which use a collaborative filtering recommendation approach, to show that our two models could constitute either an alternative or a supplementary recommendation approach.

Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses

http://arxiv.org/pdf/1604.01272v1

Title Feature extraction using Latent Dirichlet Allocation Neural Networks case study movie synopsis Summary Feature extraction gained increasing attention field machine learning order detect pattern extract information predict future observation big data urge informative feature crucial process extracting feature highly linked dimensionality reduction implies transformation data sparse highdimensional space higher level meaningful abstraction dissertation employ Neural Networks distributed paragraph representation Latent Dirichlet Allocation capture higher level feature paragraph vector Although Neural Networks distributed paragraph representation considered state art extracting paragraph vector show quick topic analysis model Latent Dirichlet Allocation provide meaningful feature evaluate two method CMU Movie Summary Corpus collection 25203 movie plot summary extracted Wikipedia Finally approach use KNearest Neighbors discover similar movie plot projected representation using TDistributed Stochastic Neighbor Embedding depict context similarity similarity expressed movie distance used movie recommendation recommended movie approach compared recommended movie IMDB use collaborative filtering recommendation approach show two model could constitute either alternative supplementary recommendation approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian Vlad Serban', 'Ryan Lowe', 'Peter Henderson', 'Laurent Charlin', 'Joelle Pineau']

1512.05742v3

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.

A Survey of Available Corpora for Building Data-Driven Dialogue Systems

http://arxiv.org/pdf/1512.05742v3

Title Survey Available Corpora Building DataDriven Dialogue Systems Summary past decade several area speech language understanding witnessed substantial breakthrough use datadriven model area dialogue system trend le obvious practical system still built significant engineering expert knowledge Nevertheless several recent result suggest datadriven approach feasible quite promising facilitate research area carried wide survey publicly available datasets suitable datadriven learning dialogue system discus important characteristic datasets used learn diverse dialogue strategy potential us also examine method transfer learning datasets use external knowledge Finally discus appropriate choice evaluation metric learning objective Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shaohua Li', 'Tat-Seng Chua', 'Jun Zhu', 'Chunyan Miao']

1606.02979v2

Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low-dimensional topic space, by utilizing the global word collocation patterns in the same document. These two types of patterns are complementary. In this paper, we propose a generative topic embedding model to combine the two types of patterns. In our model, topics are represented by embedding vectors, and are shared across documents. The probability of each word is influenced by both its local context and its topic. A variational inference method yields the topic embeddings as well as the topic mixing proportions for each document. Jointly they represent the document in a low-dimensional continuous space. In two document classification tasks, our method performs better than eight existing methods, with fewer features. In addition, we illustrate with an example that our method can generate coherent topics even based on only one document.

Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)

http://arxiv.org/pdf/1606.02979v2

Title Generative Topic Embedding Continuous Representation Documents Extended Version Proofs Summary Word embedding map word lowdimensional continuous embedding space exploiting local word collocation pattern small context window hand topic modeling map document onto lowdimensional topic space utilizing global word collocation pattern document two type pattern complementary paper propose generative topic embedding model combine two type pattern model topic represented embedding vector shared across document probability word influenced local context topic variational inference method yield topic embeddings well topic mixing proportion document Jointly represent document lowdimensional continuous space two document classification task method performs better eight existing method fewer feature addition illustrate example method generate coherent topic even based one document Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Maxim Rabinovich', 'Dan Klein']

1704.07751v1

As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data sources such as Wikipedia that have semi-open type systems. We introduce a set-prediction approach to this problem and show that our model outperforms unstructured baselines on a new Wikipedia-based fine-grained typing corpus.

Fine-Grained Entity Typing with High-Multiplicity Assignments

http://arxiv.org/pdf/1704.07751v1

Title FineGrained Entity Typing HighMultiplicity Assignments Summary entity type system become richer finegrained expect number type assigned given entity increase However finegrained typing work focused datasets exhibit low degree type multiplicity paper consider highmultiplicity regime inherent data source Wikipedia semiopen type system introduce setprediction approach problem show model outperforms unstructured baseline new Wikipediabased finegrained typing corpus Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mateusz Malinowski', 'Mario Fritz']

1410.8027v3

As language and visual understanding by machines progresses rapidly, we are observing an increasing interest in holistic architectures that tightly interlink both modalities in a joint learning and inference process. This trend has allowed the community to progress towards more challenging and open tasks and refueled the hope at achieving the old AI dream of building machines that could pass a turing test in open domains. In order to steadily make progress towards this goal, we realize that quantifying performance becomes increasingly difficult. Therefore we ask how we can precisely define such challenges and how we can evaluate different algorithms on this open tasks? In this paper, we summarize and discuss such challenges as well as try to give answers where appropriate options are available in the literature. We exemplify some of the solutions on a recently presented dataset of question-answering task based on real-world indoor images that establishes a visual turing challenge. Finally, we argue despite the success of unique ground-truth annotation, we likely have to step away from carefully curated dataset and rather rely on 'social consensus' as the main driving force to create suitable benchmarks. Providing coverage in this inherently ambiguous output space is an emerging challenge that we face in order to make quantifiable progress in this area.

Towards a Visual Turing Challenge

http://arxiv.org/pdf/1410.8027v3

Title Towards Visual Turing Challenge Summary language visual understanding machine progress rapidly observing increasing interest holistic architecture tightly interlink modality joint learning inference process trend allowed community progress towards challenging open task refueled hope achieving old AI dream building machine could pas turing test open domain order steadily make progress towards goal realize quantifying performance becomes increasingly difficult Therefore ask precisely define challenge evaluate different algorithm open task paper summarize discus challenge well try give answer appropriate option available literature exemplify solution recently presented dataset questionanswering task based realworld indoor image establishes visual turing challenge Finally argue despite success unique groundtruth annotation likely step away carefully curated dataset rather rely social consensus main driving force create suitable benchmark Providing coverage inherently ambiguous output space emerging challenge face order make quantifiable progress area Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Giovanni Saponaro', 'Lorenzo Jamone', 'Alexandre Bernardino', 'Giampiero Salvi']

1711.09055v1

A growing field in robotics and Artificial Intelligence (AI) research is human-robot collaboration, whose target is to enable effective teamwork between humans and robots. However, in many situations human teams are still superior to human-robot teams, primarily because human teams can easily agree on a common goal with language, and the individual members observe each other effectively, leveraging their shared motor repertoire and sensorimotor resources. This paper shows that for cognitive robots it is possible, and indeed fruitful, to combine knowledge acquired from interacting with elements of the environment (affordance exploration) with the probabilistic observation of another agent's actions. We propose a model that unites (i) learning robot affordances and word descriptions with (ii) statistical recognition of human gestures with vision sensors. We discuss theoretical motivations, possible implementations, and we show initial results which highlight that, after having acquired knowledge of its surrounding environment, a humanoid robot can generalize this knowledge to the case when it observes another agent (human partner) performing the same motor actions previously executed during training.

Interactive Robot Learning of Gestures, Language and Affordances

http://arxiv.org/pdf/1711.09055v1

Title Interactive Robot Learning Gestures Language Affordances Summary growing field robotics Artificial Intelligence AI research humanrobot collaboration whose target enable effective teamwork human robot However many situation human team still superior humanrobot team primarily human team easily agree common goal language individual member observe effectively leveraging shared motor repertoire sensorimotor resource paper show cognitive robot possible indeed fruitful combine knowledge acquired interacting element environment affordance exploration probabilistic observation another agent action propose model unites learning robot affordances word description ii statistical recognition human gesture vision sensor discus theoretical motivation possible implementation show initial result highlight acquired knowledge surrounding environment humanoid robot generalize knowledge case observes another agent human partner performing motor action previously executed training Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Abhinav Gupta', 'Yajie Miao', 'Leonardo Neves', 'Florian Metze']

1712.00489v1

Automatic transcriptions of consumer-generated multi-media content such as "Youtube" videos still exhibit high word error rates. Such data typically occupies a very broad domain, has been recorded in challenging conditions, with cheap hardware and a focus on the visual modality, and may have been post-processed or edited. In this paper, we extend our earlier work on adapting the acoustic model of a DNN-based speech recognition system to an RNN language model and show how both can be adapted to the objects and scenes that can be automatically detected in the video. We are working on a corpus of "how-to" videos from the web, and the idea is that an object that can be seen ("car"), or a scene that is being detected ("kitchen") can be used to condition both models on the "context" of the recording, thereby reducing perplexity and improving transcription. We achieve good improvements in both cases and compare and analyze the respective reductions in word error rate. We expect that our results can be used for any type of speech processing in which "context" information is available, for example in robotics, man-machine interaction, or when indexing large audio-visual archives, and should ultimately help to bring together the "video-to-text" and "speech-to-text" communities.

Visual Features for Context-Aware Speech Recognition

http://arxiv.org/pdf/1712.00489v1

Title Visual Features ContextAware Speech Recognition Summary Automatic transcription consumergenerated multimedia content Youtube video still exhibit high word error rate data typically occupies broad domain recorded challenging condition cheap hardware focus visual modality may postprocessed edited paper extend earlier work adapting acoustic model DNNbased speech recognition system RNN language model show adapted object scene automatically detected video working corpus howto video web idea object seen car scene detected kitchen used condition model context recording thereby reducing perplexity improving transcription achieve good improvement case compare analyze respective reduction word error rate expect result used type speech processing context information available example robotics manmachine interaction indexing large audiovisual archive ultimately help bring together videototext speechtotext community Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mircea Mironenco', 'Dana Kianfar', 'Ke Tran', 'Evangelos Kanoulas', 'Efstratios Gavves']

1712.01329v1

In this work we propose a blackbox intervention method for visual dialog models, with the aim of assessing the contribution of individual linguistic or visual components. Concretely, we conduct structured or randomized interventions that aim to impair an individual component of the model, and observe changes in task performance. We reproduce a state-of-the-art visual dialog model and demonstrate that our methodology yields surprising insights, namely that both dialog and image information have minimal contributions to task performance. The intervention method presented here can be applied as a sanity check for the strength and robustness of each component in visual dialog systems.

Examining Cooperation in Visual Dialog Models

http://arxiv.org/pdf/1712.01329v1

Title Examining Cooperation Visual Dialog Models Summary work propose blackbox intervention method visual dialog model aim assessing contribution individual linguistic visual component Concretely conduct structured randomized intervention aim impair individual component model observe change task performance reproduce stateoftheart visual dialog model demonstrate methodology yield surprising insight namely dialog image information minimal contribution task performance intervention method presented applied sanity check strength robustness component visual dialog system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Cheng-Yang Fu', 'Joon Lee', 'Mohit Bansal', 'Alexander C. Berg']

1707.08559v1

Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis. We present methods addressing the problem of automatic video highlight prediction based on joint visual features and textual analysis of the real-world audience discourse with complex slang, in both English and traditional Chinese. We present a novel dataset based on League of Legends championships recorded from North American and Taiwanese Twitch.tv channels (will be released for further research), and demonstrate strong results on these using multimodal, character-level CNN-RNN model architectures.

Video Highlight Prediction Using Audience Chat Reactions

http://arxiv.org/pdf/1707.08559v1

Title Video Highlight Prediction Using Audience Chat Reactions Summary Sports channel video portal offer exciting domain research multimodal multilingual analysis present method addressing problem automatic video highlight prediction based joint visual feature textual analysis realworld audience discourse complex slang English traditional Chinese present novel dataset based League Legends championship recorded North American Taiwanese Twitchtv channel released research demonstrate strong result using multimodal characterlevel CNNRNN model architecture Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dmitriy Serdyuk', 'Kartik Audhkhasi', 'Philémon Brakel', 'Bhuvana Ramabhadran', 'Samuel Thomas', 'Yoshua Bengio']

1612.01928v1

Modern automatic speech recognition (ASR) systems need to be robust under acoustic variability arising from environmental, speaker, channel, and recording conditions. Ensuring such robustness to variability is a challenge in modern day neural network-based ASR systems, especially when all types of variability are not seen during training. We attempt to address this problem by encouraging the neural network acoustic model to learn invariant feature representations. We use ideas from recent research on image generation using Generative Adversarial Networks and domain adaptation ideas extending adversarial gradient-based training. A recent work from Ganin et al. proposes to use adversarial training for image domain adaptation by using an intermediate representation from the main target classification network to deteriorate the domain classifier performance through a separate neural network. Our work focuses on investigating neural architectures which produce representations invariant to noise conditions for ASR. We evaluate the proposed architecture on the Aurora-4 task, a popular benchmark for noise robust ASR. We show that our method generalizes better than the standard multi-condition training especially when only a few noise categories are seen during training.

Invariant Representations for Noisy Speech Recognition

http://arxiv.org/pdf/1612.01928v1

Title Invariant Representations Noisy Speech Recognition Summary Modern automatic speech recognition ASR system need robust acoustic variability arising environmental speaker channel recording condition Ensuring robustness variability challenge modern day neural networkbased ASR system especially type variability seen training attempt address problem encouraging neural network acoustic model learn invariant feature representation use idea recent research image generation using Generative Adversarial Networks domain adaptation idea extending adversarial gradientbased training recent work Ganin et al proposes use adversarial training image domain adaptation using intermediate representation main target classification network deteriorate domain classifier performance separate neural network work focus investigating neural architecture produce representation invariant noise condition ASR evaluate proposed architecture Aurora4 task popular benchmark noise robust ASR show method generalizes better standard multicondition training especially noise category seen training Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kalin Stefanov', 'Jonas Beskow', 'Giampiero Salvi']

1711.08992v1

This paper presents a self-supervised method for detecting the active speaker in a multi-person spoken interaction scenario. We argue that this capability is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings. Our methods are able to detect an arbitrary number of possibly overlapping active speakers based exclusively on visual information about their face. Our methods do not rely on external annotations, thus complying with cognitive development. Instead, they use information from the auditory modality to support learning in the visual domain. The methods have been extensively evaluated on a large multi-person face-to-face interaction dataset. The results reach an accuracy of 80% on a multi-speaker setting. We believe this system represents an essential component of any artificial cognitive system or robotic platform engaging in social interaction.

Self-Supervised Vision-Based Detection of the Active Speaker as a Prerequisite for Socially-Aware Language Acquisition

http://arxiv.org/pdf/1711.08992v1

Title SelfSupervised VisionBased Detection Active Speaker Prerequisite SociallyAware Language Acquisition Summary paper present selfsupervised method detecting active speaker multiperson spoken interaction scenario argue capability fundamental prerequisite artificial cognitive system attempting acquire language social setting method able detect arbitrary number possibly overlapping active speaker based exclusively visual information face method rely external annotation thus complying cognitive development Instead use information auditory modality support learning visual domain method extensively evaluated large multiperson facetoface interaction dataset result reach accuracy 80 multispeaker setting believe system represents essential component artificial cognitive system robotic platform engaging social interaction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ângelo Cardoso', 'Fabio Daolio', 'Saúl Vargas']

1803.07679v1

In this paper, we describe a solution to tackle a common set of challenges in e-commerce, which arise from the fact that new products are continually being added to the catalogue. The challenges involve properly personalising the customer experience, forecasting demand and planning the product range. We argue that the foundational piece to solve all of these problems is having consistent and detailed information about each product, information that is rarely available or consistent given the multitude of suppliers and types of products. We describe in detail the architecture and methodology implemented at ASOS, one of the world's largest fashion e-commerce retailers, to tackle this problem. We then show how this quantitative understanding of the products can be leveraged to improve recommendations in a hybrid recommender system approach.

Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products

http://arxiv.org/pdf/1803.07679v1

Title Product Characterisation towards Personalisation Learning Attributes Unstructured Data Recommend Fashion Products Summary paper describe solution tackle common set challenge ecommerce arise fact new product continually added catalogue challenge involve properly personalising customer experience forecasting demand planning product range argue foundational piece solve problem consistent detailed information product information rarely available consistent given multitude supplier type product describe detail architecture methodology implemented ASOS one world largest fashion ecommerce retailer tackle problem show quantitative understanding product leveraged improve recommendation hybrid recommender system approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pierre-Yves Oudeyer']

cs/0502086v1

The speech code is a vehicle of language: it defines a set of forms used by a community to carry information. Such a code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is discrete and compositional, shared by all the individuals of a community but different across communities, and phoneme inventories are characterized by statistical regularities. How can a speech code with these properties form? We try to approach these questions in the paper, using the "methodology of the artificial". We build a society of artificial agents, and detail a mechanism that shows the formation of a discrete speech code without pre-supposing the existence of linguistic capacities or of coordinated interactions. The mechanism is based on a low-level model of sensory-motor interactions. We show that the integration of certain very simple and non language-specific neural devices leads to the formation of a speech code that has properties similar to the human speech code. This result relies on the self-organizing properties of a generic coupling between perception and production within agents, and on the interactions between agents. The artificial system helps us to develop better intuitions on how speech might have appeared, by showing how self-organization might have helped natural selection to find speech.

The Self-Organization of Speech Sounds

http://arxiv.org/pdf/cs/0502086v1

Title SelfOrganization Speech Sounds Summary speech code vehicle language defines set form used community carry information code necessary support linguistic interaction allow human communicate may speech code formed prior existence linguistic interaction Moreover human speech code discrete compositional shared individual community different across community phoneme inventory characterized statistical regularity speech code property form try approach question paper using methodology artificial build society artificial agent detail mechanism show formation discrete speech code without presupposing existence linguistic capacity coordinated interaction mechanism based lowlevel model sensorymotor interaction show integration certain simple non languagespecific neural device lead formation speech code property similar human speech code result relies selforganizing property generic coupling perception production within agent interaction agent artificial system help u develop better intuition speech might appeared showing selforganization might helped natural selection find speech Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['David M. W. Powers']

1503.06410v1

The F-measure or F-score is one of the most commonly used single number measures in Information Retrieval, Natural Language Processing and Machine Learning, but it is based on a mistake, and the flawed assumptions render it unsuitable for use in most contexts! Fortunately, there are better alternatives.

What the F-measure doesn't measure: Features, Flaws, Fallacies and Fixes

http://arxiv.org/pdf/1503.06410v1

Title Fmeasure doesnt measure Features Flaws Fallacies Fixes Summary Fmeasure Fscore one commonly used single number measure Information Retrieval Natural Language Processing Machine Learning based mistake flawed assumption render unsuitable use context Fortunately better alternative Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Byron Knoll', 'Nando de Freitas']

1108.3298v1

PAQ8 is an open source lossless data compression algorithm that currently achieves the best compression rates on many benchmarks. This report presents a detailed description of PAQ8 from a statistical machine learning perspective. It shows that it is possible to understand some of the modules of PAQ8 and use this understanding to improve the method. However, intuitive statistical explanations of the behavior of other modules remain elusive. We hope the description in this report will be a starting point for discussions that will increase our understanding, lead to improvements to PAQ8, and facilitate a transfer of knowledge from PAQ8 to other machine learning methods, such a recurrent neural networks and stochastic memoizers. Finally, the report presents a broad range of new applications of PAQ to machine learning tasks including language modeling and adaptive text prediction, adaptive game playing, classification, and compression using features from the field of deep learning.

A Machine Learning Perspective on Predictive Coding with PAQ

http://arxiv.org/pdf/1108.3298v1

Title Machine Learning Perspective Predictive Coding PAQ Summary PAQ8 open source lossless data compression algorithm currently achieves best compression rate many benchmark report present detailed description PAQ8 statistical machine learning perspective show possible understand module PAQ8 use understanding improve method However intuitive statistical explanation behavior module remain elusive hope description report starting point discussion increase understanding lead improvement PAQ8 facilitate transfer knowledge PAQ8 machine learning method recurrent neural network stochastic memoizers Finally report present broad range new application PAQ machine learning task including language modeling adaptive text prediction adaptive game playing classification compression using feature field deep learning Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hector Allende', 'Emanuele Frandi', 'Ricardo Nanculef', 'Claudio Sartori']

1304.1014v2

Recently, there has been a renewed interest in the machine learning community for variants of a sparse greedy approximation procedure for concave optimization known as {the Frank-Wolfe (FW) method}. In particular, this procedure has been successfully applied to train large-scale instances of non-linear Support Vector Machines (SVMs). Specializing FW to SVM training has allowed to obtain efficient algorithms but also important theoretical results, including convergence analysis of training algorithms and new characterizations of model sparsity. In this paper, we present and analyze a novel variant of the FW method based on a new way to perform away steps, a classic strategy used to accelerate the convergence of the basic FW procedure. Our formulation and analysis is focused on a general concave maximization problem on the simplex. However, the specialization of our algorithm to quadratic forms is strongly related to some classic methods in computational geometry, namely the Gilbert and MDM algorithms. On the theoretical side, we demonstrate that the method matches the guarantees in terms of convergence rate and number of iterations obtained by using classic away steps. In particular, the method enjoys a linear rate of convergence, a result that has been recently proved for MDM on quadratic forms. On the practical side, we provide experiments on several classification datasets, and evaluate the results using statistical tests. Experiments show that our method is faster than the FW method with classic away steps, and works well even in the cases in which classic away steps slow down the algorithm. Furthermore, these improvements are obtained without sacrificing the predictive accuracy of the obtained SVM model.

A Novel Frank-Wolfe Algorithm. Analysis and Applications to Large-Scale SVM Training

http://arxiv.org/pdf/1304.1014v2

Title Novel FrankWolfe Algorithm Analysis Applications LargeScale SVM Training Summary Recently renewed interest machine learning community variant sparse greedy approximation procedure concave optimization known FrankWolfe FW method particular procedure successfully applied train largescale instance nonlinear Support Vector Machines SVMs Specializing FW SVM training allowed obtain efficient algorithm also important theoretical result including convergence analysis training algorithm new characterization model sparsity paper present analyze novel variant FW method based new way perform away step classic strategy used accelerate convergence basic FW procedure formulation analysis focused general concave maximization problem simplex However specialization algorithm quadratic form strongly related classic method computational geometry namely Gilbert MDM algorithm theoretical side demonstrate method match guarantee term convergence rate number iteration obtained using classic away step particular method enjoys linear rate convergence result recently proved MDM quadratic form practical side provide experiment several classification datasets evaluate result using statistical test Experiments show method faster FW method classic away step work well even case classic away step slow algorithm Furthermore improvement obtained without sacrificing predictive accuracy obtained SVM model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yanwei Fu', 'Leonid Sigal']

1604.07093v1

Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of semi-supervised vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot and open set recognition using a unified framework. Specifically, we propose a maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms, ensuring that labeled samples are projected closest to their correct prototypes, in the embedding space, than to others. We show that resulting model shows improvements in supervised, zero-shot, and large open set recognition, with up to 310K class vocabulary on AwA and ImageNet datasets.

Semi-supervised Vocabulary-informed Learning

http://arxiv.org/pdf/1604.07093v1

Title Semisupervised Vocabularyinformed Learning Summary Despite significant progress object categorization recent year number important challenge remain mainly ability learn limited labeled data ability recognize object class within large potentially open set label Zeroshot learning one way addressing challenge shown work limited sized class vocabulary typically requires separation supervised unsupervised class allowing former inform latter vice versa propose notion semisupervised vocabularyinformed learning alleviate mentioned challenge address problem supervised zeroshot open set recognition using unified framework Specifically propose maximum margin framework semantic manifoldbased recognition incorporates distance constraint supervised unsupervised vocabulary atom ensuring labeled sample projected closest correct prototype embedding space others show resulting model show improvement supervised zeroshot large open set recognition 310K class vocabulary AwA ImageNet datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Adarsh Prasad', 'Stefanie Jegelka', 'Dhruv Batra']

1411.1752v1

To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-quality candidate solutions or proposals. In structured prediction problems, this becomes a daunting task, as the solution space (image labelings, sentence parses, etc.) is exponentially large. We study greedy algorithms for finding a diverse subset of solutions in structured-output spaces by drawing new connections between submodular functions over combinatorial item sets and High-Order Potentials (HOPs) studied for graphical models. Specifically, we show via examples that when marginal gains of submodular diversity functions allow structured representations, this enables efficient (sub-linear time) approximate maximization by reducing the greedy augmentation step to inference in a factor graph with appropriately constructed HOPs. We discuss benefits, tradeoffs, and show that our constructions lead to significantly better proposals.

Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets

http://arxiv.org/pdf/1411.1752v1

Title Submodular meet Structured Finding Diverse Subsets ExponentiallyLarge Structured Item Sets Summary cope high level ambiguity faced domain Computer Vision Natural Language processing robust prediction method often search diverse set highquality candidate solution proposal structured prediction problem becomes daunting task solution space image labelings sentence par etc exponentially large study greedy algorithm finding diverse subset solution structuredoutput space drawing new connection submodular function combinatorial item set HighOrder Potentials HOPs studied graphical model Specifically show via example marginal gain submodular diversity function allow structured representation enables efficient sublinear time approximate maximization reducing greedy augmentation step inference factor graph appropriately constructed HOPs discus benefit tradeoff show construction lead significantly better proposal Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hao Wang', 'Xiaodan Liang', 'Hao Zhang', 'Dit-Yan Yeung', 'Eric P. Xing']

1703.07255v2

Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image with any personalized signals (even signals unseen during training), such as diverse paintings and arbitrary descriptive attributes. However, existing methods are either inefficient to simultaneously process multiple signals (let alone generalize to unseen signals), or unable to handle signals from other modalities. In this paper, we make the first attempt to address the zero-shot image manipulation task. We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a fully-differentiable architecture that jointly optimizes an image-transformation network (TNet) and a parameter network (PNet). The PNet learns to generate key transformation parameters for the TNet given any guiding signal while the TNet performs fast zero-shot image manipulation according to both signal-dependent parameters from the PNet and signal-invariant parameters from the TNet itself. Extensive experiments show that our ZM-Net can perform high-quality image manipulation conditioned on different forms of guiding signals (e.g. style images and attributes) in real-time (tens of milliseconds per image) even for unseen signals. Moreover, a large-scale style dataset with over 20,000 style images is also constructed to promote further research.

ZM-Net: Real-time Zero-shot Image Manipulation Network

http://arxiv.org/pdf/1703.07255v2

Title ZMNet Realtime Zeroshot Image Manipulation Network Summary Many problem image processing computer vision eg colorization style transfer posed manipulating input image corresponding output image given userspecified guiding signal holygrail solution towards generic image manipulation able efficiently alter input image personalized signal even signal unseen training diverse painting arbitrary descriptive attribute However existing method either inefficient simultaneously process multiple signal let alone generalize unseen signal unable handle signal modality paper make first attempt address zeroshot image manipulation task cast problem manipulating input image according parametric model whose key parameter conditionally generated guiding signal even unseen one end propose Zeroshot Manipulation Net ZMNet fullydifferentiable architecture jointly optimizes imagetransformation network TNet parameter network PNet PNet learns generate key transformation parameter TNet given guiding signal TNet performs fast zeroshot image manipulation according signaldependent parameter PNet signalinvariant parameter TNet Extensive experiment show ZMNet perform highquality image manipulation conditioned different form guiding signal eg style image attribute realtime ten millisecond per image even unseen signal Moreover largescale style dataset 20000 style image also constructed promote research Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Arnab Ghosh', 'Viveka Kulharia', 'Vinay Namboodiri', 'Philip H. S. Torr', 'Puneet K. Dokania']

1704.02906v2

We propose an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known mode collapse problem. Firstly, we propose a multi-agent GAN architecture incorporating multiple generators and one discriminator. Secondly, to enforce different generators to capture diverse high probability modes, we modify discriminator's objective function where along with finding the real and fake samples, the discriminator has to identify the generator that generated the fake sample. Intuitively, to succeed in this task, the discriminator must learn to push different generators towards different identifiable modes. Our framework (MAD-GAN) is generalizable in the sense that it can be easily combined with other existing variants of GANs to produce diverse samples. We perform extensive experiments on synthetic and real datasets and compare MAD-GAN with different variants of GAN. We show high quality diverse sample generations for the challenging tasks such as image-to-image translation (known to learn delta distribution) and face generation. In addition, we show that MAD-GAN is able to disentangle different modalities even when trained using highly challenging multi-view dataset (mixture of forests, icebergs, bedrooms etc). In the end, we also show its efficacy for the unsupervised feature representation task. In the appendix we introduce a similarity based competing objective which encourages the different generators to generate varied samples judged by a user defined similarity metric. We show extensive evaluations on a 1-D setting of mixture of gaussians for non parametric density estimation. The theoretical proofs back the efficacy of the framework and explains why various generators are pushed towards distinct clusters of modes.

Multi-Agent Diverse Generative Adversarial Networks

http://arxiv.org/pdf/1704.02906v2

Title MultiAgent Diverse Generative Adversarial Networks Summary propose intuitive generalization Generative Adversarial Networks GANs conditional variant address well known mode collapse problem Firstly propose multiagent GAN architecture incorporating multiple generator one discriminator Secondly enforce different generator capture diverse high probability mode modify discriminator objective function along finding real fake sample discriminator identify generator generated fake sample Intuitively succeed task discriminator must learn push different generator towards different identifiable mode framework MADGAN generalizable sense easily combined existing variant GANs produce diverse sample perform extensive experiment synthetic real datasets compare MADGAN different variant GAN show high quality diverse sample generation challenging task imagetoimage translation known learn delta distribution face generation addition show MADGAN able disentangle different modality even trained using highly challenging multiview dataset mixture forest iceberg bedroom etc end also show efficacy unsupervised feature representation task appendix introduce similarity based competing objective encourages different generator generate varied sample judged user defined similarity metric show extensive evaluation 1D setting mixture gaussians non parametric density estimation theoretical proof back efficacy framework explains various generator pushed towards distinct cluster mode Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jae Hyun Lim', 'Jong Chul Ye']

1705.02894v2

Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One of the main contributions of this paper is to reveal a unified geometric structure in GAN and its variants. Specifically, we show that the adversarial generative model training can be decomposed into three geometric steps: separating hyperplane search, discriminator parameter update away from the separating hyperplane, and the generator update along the normal vector direction of the separating hyperplane. This geometric intuition reveals the limitations of the existing approaches and leads us to propose a new formulation called geometric GAN using SVM separating hyperplane that maximizes the margin. Our theoretical analysis shows that the geometric GAN converges to a Nash equilibrium between the discriminator and generator. In addition, extensive numerical results show that the superior performance of geometric GAN.

Geometric GAN

http://arxiv.org/pdf/1705.02894v2

Title Geometric GAN Summary Generative Adversarial Nets GANs represent important milestone effective generative model inspired numerous variant seemingly different One main contribution paper reveal unified geometric structure GAN variant Specifically show adversarial generative model training decomposed three geometric step separating hyperplane search discriminator parameter update away separating hyperplane generator update along normal vector direction separating hyperplane geometric intuition reveals limitation existing approach lead u propose new formulation called geometric GAN using SVM separating hyperplane maximizes margin theoretical analysis show geometric GAN converges Nash equilibrium discriminator generator addition extensive numerical result show superior performance geometric GAN Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Disha Shrivastava', 'Santanu Chaudhury', 'Dr. Jayadeva']

1708.05840v1

Training deep networks is expensive and time-consuming with the training period increasing with data size and growth in model parameters. In this paper, we provide a framework for distributed training of deep networks over a cluster of CPUs in Apache Spark. The framework implements both Data Parallelism and Model Parallelism making it suitable to use for deep networks which require huge training data and model parameters which are too big to fit into the memory of a single machine. It can be scaled easily over a cluster of cheap commodity hardware to attain significant speedup and obtain better results making it quite economical as compared to farm of GPUs and supercomputers. We have proposed a new algorithm for training of deep networks for the case when the network is partitioned across the machines (Model Parallelism) along with detailed cost analysis and proof of convergence of the same. We have developed implementations for Fully-Connected Feedforward Networks, Convolutional Neural Networks, Recurrent Neural Networks and Long Short-Term Memory architectures. We present the results of extensive simulations demonstrating the speedup and accuracy obtained by our framework for different sizes of the data and model parameters with variation in the number of worker cores/partitions; thereby showing that our proposed framework can achieve significant speedup (upto 11X for CNN) and is also quite scalable.

A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark

http://arxiv.org/pdf/1708.05840v1

Title Data ModelParallel Distributed Scalable Framework Training Deep Networks Apache Spark Summary Training deep network expensive timeconsuming training period increasing data size growth model parameter paper provide framework distributed training deep network cluster CPUs Apache Spark framework implement Data Parallelism Model Parallelism making suitable use deep network require huge training data model parameter big fit memory single machine scaled easily cluster cheap commodity hardware attain significant speedup obtain better result making quite economical compared farm GPUs supercomputer proposed new algorithm training deep network case network partitioned across machine Model Parallelism along detailed cost analysis proof convergence developed implementation FullyConnected Feedforward Networks Convolutional Neural Networks Recurrent Neural Networks Long ShortTerm Memory architecture present result extensive simulation demonstrating speedup accuracy obtained framework different size data model parameter variation number worker corespartitions thereby showing proposed framework achieve significant speedup upto 11X CNN also quite scalable Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sebastian Lapuschkin', 'Alexander Binder', 'Klaus-Robert Müller', 'Wojciech Samek']

1708.07689v1

Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box manner with no information provided about which facial features are actually used for prediction and how these features depend on image preprocessing, model initialization and architecture choice. We present a study investigating these different effects. In detail, our work compares four popular neural network architectures, studies the effect of pretraining, evaluates the robustness of the considered alignment preprocessings via cross-method test set swapping and intuitively visualizes the model's prediction strategies in given preprocessing conditions using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our evaluations on the challenging Adience benchmark show that suitable parameter initialization leads to a holistic perception of the input, compensating artefactual data representations. With a combination of simple preprocessing steps, we reach state of the art performance in gender recognition.

Understanding and Comparing Deep Neural Networks for Age and Gender Classification

http://arxiv.org/pdf/1708.07689v1

Title Understanding Comparing Deep Neural Networks Age Gender Classification Summary Recently deep neural network demonstrated excellent performance recognizing age gender human face image However model applied blackbox manner information provided facial feature actually used prediction feature depend image preprocessing model initialization architecture choice present study investigating different effect detail work compare four popular neural network architecture study effect pretraining evaluates robustness considered alignment preprocessings via crossmethod test set swapping intuitively visualizes model prediction strategy given preprocessing condition using recent Layerwise Relevance Propagation LRP algorithm evaluation challenging Adience benchmark show suitable parameter initialization lead holistic perception input compensating artefactual data representation combination simple preprocessing step reach state art performance gender recognition Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Simon S. Du', 'Jason D. Lee', 'Yuandong Tian']

1709.06129v2

We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that are restricted to standard Gaussian input. We show that (stochastic) gradient descent with random initialization can learn the convolutional filter in polynomial time and the convergence rate depends on the smoothness of the input distribution and the closeness of patches. To the best of our knowledge, this is the first recovery guarantee of gradient-based algorithms for convolutional filter on non-Gaussian input distributions. Our theory also justifies the two-stage learning rate strategy in deep neural networks. While our focus is theoretical, we also present experiments that illustrate our theoretical findings.

When is a Convolutional Filter Easy To Learn?

http://arxiv.org/pdf/1709.06129v2

Title Convolutional Filter Easy Learn Summary analyze convergence stochastic gradient descent algorithm learning convolutional filter Rectified Linear Unit ReLU activation function analysis rely specific form input distribution proof use definition ReLU contrast previous work restricted standard Gaussian input show stochastic gradient descent random initialization learn convolutional filter polynomial time convergence rate depends smoothness input distribution closeness patch best knowledge first recovery guarantee gradientbased algorithm convolutional filter nonGaussian input distribution theory also justifies twostage learning rate strategy deep neural network focus theoretical also present experiment illustrate theoretical finding Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jianqiao Wangni', 'Dahua Lin']

1711.02857v1

Sparsity inducing regularization is an important part for learning over-complete visual representations. Despite the popularity of $\ell_1$ regularization, in this paper, we investigate the usage of non-convex regularizations in this problem. Our contribution consists of three parts. First, we propose the leaky capped norm regularization (LCNR), which allows model weights below a certain threshold to be regularized more strongly as opposed to those above, therefore imposes strong sparsity and only introduces controllable estimation bias. We propose a majorization-minimization algorithm to optimize the joint objective function. Second, our study over monocular 3D shape recovery and neural networks with LCNR outperforms $\ell_1$ and other non-convex regularizations, achieving state-of-the-art performance and faster convergence. Third, we prove a theoretical global convergence speed on the 3D recovery problem. To the best of our knowledge, this is the first convergence analysis of the 3D recovery problem.

Learning Sparse Visual Representations with Leaky Capped Norm Regularizers

http://arxiv.org/pdf/1711.02857v1

Title Learning Sparse Visual Representations Leaky Capped Norm Regularizers Summary Sparsity inducing regularization important part learning overcomplete visual representation Despite popularity ell1 regularization paper investigate usage nonconvex regularization problem contribution consists three part First propose leaky capped norm regularization LCNR allows model weight certain threshold regularized strongly opposed therefore imposes strong sparsity introduces controllable estimation bias propose majorizationminimization algorithm optimize joint objective function Second study monocular 3D shape recovery neural network LCNR outperforms ell1 nonconvex regularization achieving stateoftheart performance faster convergence Third prove theoretical global convergence speed 3D recovery problem best knowledge first convergence analysis 3D recovery problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pierre Stock', 'Moustapha Cisse']

1711.11443v1

ConvNets and Imagenet have driven the recent success of deep learning for image classification. However, the marked slowdown in performance improvement, the recent studies on the lack of robustness of neural networks to adversarial examples and their tendency to exhibit undesirable biases (e.g racial biases) questioned the reliability and the sustained development of these methods. This work investigates these questions from the perspective of the end-user by using human subject studies and explanations. We experimentally demonstrate that the accuracy and robustness of ConvNets measured on Imagenet are underestimated. We show that explanations can mitigate the impact of misclassified adversarial examples from the perspective of the end-user and we introduce a novel tool for uncovering the undesirable biases learned by a model. These contributions also show that explanations are a promising tool for improving our understanding of ConvNets' predictions and for designing more reliable models

ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism

http://arxiv.org/pdf/1711.11443v1

Title ConvNets ImageNet Beyond Accuracy Explanations Bias Detection Adversarial Examples Model Criticism Summary ConvNets Imagenet driven recent success deep learning image classification However marked slowdown performance improvement recent study lack robustness neural network adversarial example tendency exhibit undesirable bias eg racial bias questioned reliability sustained development method work investigates question perspective enduser using human subject study explanation experimentally demonstrate accuracy robustness ConvNets measured Imagenet underestimated show explanation mitigate impact misclassified adversarial example perspective enduser introduce novel tool uncovering undesirable bias learned model contribution also show explanation promising tool improving understanding ConvNets prediction designing reliable model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Simon S. Du', 'Jason D. Lee', 'Yuandong Tian', 'Barnabas Poczos', 'Aarti Singh']

1712.00779v1

We consider the problem of learning a one-hidden-layer neural network with non-overlapping convolutional layer and ReLU activation function, i.e., $f(\mathbf{Z}; \mathbf{w}, \mathbf{a}) = \sum_j a_j\sigma(\mathbf{w}^\top\mathbf{Z}_j)$, in which both the convolutional weights $\mathbf{w}$ and the output weights $\mathbf{a}$ are parameters to be learned. We prove that with Gaussian input $\mathbf{Z}$, there is a spurious local minimum that is not a global mininum. Surprisingly, in the presence of local minimum, starting from randomly initialized weights, gradient descent with weight normalization can still be proven to recover the true parameters with constant probability (which can be boosted to arbitrarily high accuracy with multiple restarts). We also show that with constant probability, the same procedure could also converge to the spurious local minimum, showing that the local minimum plays a non-trivial role in the dynamics of gradient descent. Furthermore, a quantitative analysis shows that the gradient descent dynamics has two phases: it starts off slow, but converges much faster after several iterations.

Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima

http://arxiv.org/pdf/1712.00779v1

Title Gradient Descent Learns Onehiddenlayer CNN Dont Afraid Spurious Local Minima Summary consider problem learning onehiddenlayer neural network nonoverlapping convolutional layer ReLU activation function ie fmathbfZ mathbfw mathbfa sumj ajsigmamathbfwtopmathbfZj convolutional weight mathbfw output weight mathbfa parameter learned prove Gaussian input mathbfZ spurious local minimum global mininum Surprisingly presence local minimum starting randomly initialized weight gradient descent weight normalization still proven recover true parameter constant probability boosted arbitrarily high accuracy multiple restarts also show constant probability procedure could also converge spurious local minimum showing local minimum play nontrivial role dynamic gradient descent Furthermore quantitative analysis show gradient descent dynamic two phase start slow converges much faster several iteration Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Deepak Pathak', 'Pulkit Agrawal', 'Alexei A. Efros', 'Trevor Darrell']

1705.05363v1

In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the agent to explore its environment and learn skills that might be useful later in its life. We formulate curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model. Our formulation scales to high-dimensional continuous state spaces like images, bypasses the difficulties of directly predicting pixels, and, critically, ignores the aspects of the environment that cannot affect the agent. The proposed approach is evaluated in two environments: VizDoom and Super Mario Bros. Three broad settings are investigated: 1) sparse extrinsic reward, where curiosity allows for far fewer interactions with the environment to reach the goal; 2) exploration with no extrinsic reward, where curiosity pushes the agent to explore more efficiently; and 3) generalization to unseen scenarios (e.g. new levels of the same game) where the knowledge gained from earlier experience helps the agent explore new places much faster than starting from scratch. Demo video and code available at https://pathak22.github.io/noreward-rl/

Curiosity-driven Exploration by Self-supervised Prediction

http://arxiv.org/pdf/1705.05363v1

Title Curiositydriven Exploration Selfsupervised Prediction Summary many realworld scenario reward extrinsic agent extremely sparse absent altogether case curiosity serve intrinsic reward signal enable agent explore environment learn skill might useful later life formulate curiosity error agent ability predict consequence action visual feature space learned selfsupervised inverse dynamic model formulation scale highdimensional continuous state space like image bypass difficulty directly predicting pixel critically ignores aspect environment cannot affect agent proposed approach evaluated two environment VizDoom Super Mario Bros Three broad setting investigated 1 sparse extrinsic reward curiosity allows far fewer interaction environment reach goal 2 exploration extrinsic reward curiosity push agent explore efficiently 3 generalization unseen scenario eg new level game knowledge gained earlier experience help agent explore new place much faster starting scratch Demo video code available httpspathak22githubionorewardrl Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Moustapha Cisse', 'Yossi Adi', 'Natalia Neverova', 'Joseph Keshet']

1707.05373v1

Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines. So far, most existing methods only work for classification and are not designed to alter the true performance measure of the problem at hand. We introduce a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered, be it combinatorial and non-decomposable. We successfully apply Houdini to a range of applications such as speech recognition, pose estimation and semantic segmentation. In all cases, the attacks based on Houdini achieve higher success rate than those based on the traditional surrogates used to train the models while using a less perceptible adversarial perturbation.

Houdini: Fooling Deep Structured Prediction Models

http://arxiv.org/pdf/1707.05373v1

Title Houdini Fooling Deep Structured Prediction Models Summary Generating adversarial example critical step evaluating improving robustness learning machine far existing method work classification designed alter true performance measure problem hand introduce novel flexible approach named Houdini generating adversarial example specifically tailored final performance measure task considered combinatorial nondecomposable successfully apply Houdini range application speech recognition pose estimation semantic segmentation case attack based Houdini achieve higher success rate based traditional surrogate used train model using le perceptible adversarial perturbation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yanwei Fu', 'Tao Xiang', 'Yu-Gang Jiang', 'Xiangyang Xue', 'Leonid Sigal', 'Shaogang Gong']

1710.04837v1

With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings. We also overview related recognition tasks including one-shot and open set recognition which can be used as natural extensions of zero-shot recognition when limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. Importantly, we highlight the limitations of existing approaches and point out future research directions in this existing new research area.

Recent Advances in Zero-shot Recognition

http://arxiv.org/pdf/1710.04837v1

Title Recent Advances Zeroshot Recognition Summary recent renaissance deep convolution neural network encouraging breakthrough achieved supervised recognition task class sufficient training data fully annotated training data However scale recognition large number class training sample class remains unsolved problem One approach scaling recognition develop model capable recognizing unseen category without training instance zeroshot recognition learning article provides comprehensive review existing zeroshot recognition technique covering various aspect ranging representation model datasets evaluation setting also overview related recognition task including oneshot open set recognition used natural extension zeroshot recognition limited number class sample become available zeroshot recognition implemented realworld setting Importantly highlight limitation existing approach point future research direction existing new research area Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Quynh Nguyen', 'Matthias Hein']

1710.10928v1

We analyze the expressiveness and loss surface of practical deep convolutional neural networks (CNNs) with shared weights and max pooling layers. We show that such CNNs produce linearly independent features at a "wide" layer which has more neurons than the number of training samples. This condition holds e.g. for the VGG network. Furthermore, we provide for such wide CNNs necessary and sufficient conditions for global minima with zero training error. For the case where the wide layer is followed by a fully connected layer, we show that almost every critical point of the empirical loss is a global minimum with zero training error. Our analysis suggests that both depth and width are very important in deep learning. While depth brings more representational power and allows the network to learn high level features, width smoothes the optimization landscape of the loss function in the sense that a sufficiently wide network has a well-behaved loss surface with potentially no bad local minima.

The loss surface and expressivity of deep convolutional neural networks

http://arxiv.org/pdf/1710.10928v1

Title loss surface expressivity deep convolutional neural network Summary analyze expressiveness loss surface practical deep convolutional neural network CNNs shared weight max pooling layer show CNNs produce linearly independent feature wide layer neuron number training sample condition hold eg VGG network Furthermore provide wide CNNs necessary sufficient condition global minimum zero training error case wide layer followed fully connected layer show almost every critical point empirical loss global minimum zero training error analysis suggests depth width important deep learning depth brings representational power allows network learn high level feature width smoothes optimization landscape loss function sense sufficiently wide network wellbehaved loss surface potentially bad local minimum Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Anuj Karpatne', 'William Watkins', 'Jordan Read', 'Vipin Kumar']

1710.11431v2

This paper introduces a novel framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed as physics-guided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture. Further, this paper presents a novel framework for using physics-based loss functions in the learning objective of neural networks, to ensure that the model predictions not only show lower errors on the training set but are also scientifically consistent with the known physics on the unlabeled set. We illustrate the effectiveness of PGNN for the problem of lake temperature modeling, where physical relationships between the temperature, density, and depth of water are used to design a physics-based loss function. By using scientific knowledge to guide the construction and learning of neural networks, we are able to show that the proposed framework ensures better generalizability as well as scientific consistency of results.

Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

http://arxiv.org/pdf/1710.11431v2

Title Physicsguided Neural Networks PGNN Application Lake Temperature Modeling Summary paper introduces novel framework combining scientific knowledge physicsbased model neural network advance scientific discovery framework termed physicsguided neural network PGNN leverage output physicsbased model simulation along observational feature generate prediction using neural network architecture paper present novel framework using physicsbased loss function learning objective neural network ensure model prediction show lower error training set also scientifically consistent known physic unlabeled set illustrate effectiveness PGNN problem lake temperature modeling physical relationship temperature density depth water used design physicsbased loss function using scientific knowledge guide construction learning neural network able show proposed framework ensures better generalizability well scientific consistency result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhao Kang', 'Chong Peng', 'Qiang Cheng', 'Zenglin Xu']

1711.04258v1

Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to severe information loss and performance degradation. First, predefined similarity graph might not be optimal for subsequent clustering. It is well-accepted that similarity graph highly affects the clustering results. To this end, we propose to automatically learn similarity information from data and simultaneously consider the constraint that the similarity matrix has exact c connected components if there are c clusters. Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers. In this work, we transform the candidate solution into a new one that better approximates the discrete one. Finally, those three subtasks are integrated into a unified framework, with each subtask iteratively boosted by using the results of the others towards an overall optimal solution. It is known that the performance of a kernel method is largely determined by the choice of kernels. To tackle this practical problem of how to select the most suitable kernel for a particular data set, we further extend our model to incorporate multiple kernel learning ability. Extensive experiments demonstrate the superiority of our proposed method as compared to existing clustering approaches.

Unified Spectral Clustering with Optimal Graph

http://arxiv.org/pdf/1711.04258v1

Title Unified Spectral Clustering Optimal Graph Summary Spectral clustering found extensive use many area traditional spectral clustering algorithm work three separate step similarity graph construction continuous label learning discretizing learned label kmeans clustering common practice two potential flaw may lead severe information loss performance degradation First predefined similarity graph might optimal subsequent clustering wellaccepted similarity graph highly affect clustering result end propose automatically learn similarity information data simultaneously consider constraint similarity matrix exact c connected component c cluster Second discrete solution may deviate spectral solution since kmeans method wellknown sensitive initialization cluster center work transform candidate solution new one better approximates discrete one Finally three subtasks integrated unified framework subtask iteratively boosted using result others towards overall optimal solution known performance kernel method largely determined choice kernel tackle practical problem select suitable kernel particular data set extend model incorporate multiple kernel learning ability Extensive experiment demonstrate superiority proposed method compared existing clustering approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['David P. Helmbold', 'Philip M. Long']

1412.4736v4

Dropout is a simple but effective technique for learning in neural networks and other settings. A sound theoretical understanding of dropout is needed to determine when dropout should be applied and how to use it most effectively. In this paper we continue the exploration of dropout as a regularizer pioneered by Wager, et.al. We focus on linear classification where a convex proxy to the misclassification loss (i.e. the logistic loss used in logistic regression) is minimized. We show: (a) when the dropout-regularized criterion has a unique minimizer, (b) when the dropout-regularization penalty goes to infinity with the weights, and when it remains bounded, (c) that the dropout regularization can be non-monotonic as individual weights increase from 0, and (d) that the dropout regularization penalty may not be convex. This last point is particularly surprising because the combination of dropout regularization with any convex loss proxy is always a convex function. In order to contrast dropout regularization with $L_2$ regularization, we formalize the notion of when different sources are more compatible with different regularizers. We then exhibit distributions that are provably more compatible with dropout regularization than $L_2$ regularization, and vice versa. These sources provide additional insight into how the inductive biases of dropout and $L_2$ regularization differ. We provide some similar results for $L_1$ regularization.

On the Inductive Bias of Dropout

http://arxiv.org/pdf/1412.4736v4

Title Inductive Bias Dropout Summary Dropout simple effective technique learning neural network setting sound theoretical understanding dropout needed determine dropout applied use effectively paper continue exploration dropout regularizer pioneered Wager etal focus linear classification convex proxy misclassification loss ie logistic loss used logistic regression minimized show dropoutregularized criterion unique minimizer b dropoutregularization penalty go infinity weight remains bounded c dropout regularization nonmonotonic individual weight increase 0 dropout regularization penalty may convex last point particularly surprising combination dropout regularization convex loss proxy always convex function order contrast dropout regularization L2 regularization formalize notion different source compatible different regularizers exhibit distribution provably compatible dropout regularization L2 regularization vice versa source provide additional insight inductive bias dropout L2 regularization differ provide similar result L1 regularization Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['David P. Helmbold', 'Philip M. Long']

1602.04484v5

We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output is the sum of the inputs. This provides a counterpoint to the suggestion that dropout discourages co-adaptation of weights. We also show that the dropout penalty can grow exponentially in the depth of the network while the weight-decay penalty remains essentially linear, and that dropout is insensitive to various re-scalings of the input features, outputs, and network weights. This last insensitivity implies that there are no isolated local minima of the dropout training criterion. Our work uncovers new properties of dropout, extends our understanding of why dropout succeeds, and lays the foundation for further progress.

Surprising properties of dropout in deep networks

http://arxiv.org/pdf/1602.04484v5

Title Surprising property dropout deep network Summary analyze dropout deep network rectified linear unit quadratic loss result expose surprising difference behavior dropout traditional regularizers like weight decay example simple data set dropout training produce negative weight even though output sum input provides counterpoint suggestion dropout discourages coadaptation weight also show dropout penalty grow exponentially depth network weightdecay penalty remains essentially linear dropout insensitive various rescalings input feature output network weight last insensitivity implies isolated local minimum dropout training criterion work uncovers new property dropout extends understanding dropout succeeds lay foundation progress Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Alireza Bagheri', 'Osvaldo Simeone', 'Bipin Rajendran']

1710.10704v3

Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNN is studied for the purpose of classification, under a Generalized Linear Model (GLM) probabilistic neural model that was previously considered within the computational neuroscience literature. Conventional classification rules for SNNs operate offline based on the number of output spikes at each output neuron. In contrast, a novel training method is proposed here for a first-to-spike decoding rule, whereby the SNN can perform an early classification decision once spike firing is detected at an output neuron. Numerical results bring insights into the optimal parameter selection for the GLM neuron and on the accuracy-complexity trade-off performance of conventional and first-to-spike decoding.

Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

http://arxiv.org/pdf/1710.10704v3

Title Training Probabilistic Spiking Neural Networks Firsttospike Decoding Summary Thirdgeneration neural network Spiking Neural Networks SNNs aim harnessing energy efficiency spikedomain processing building computing element operate exchange spike paper problem training twolayer SNN studied purpose classification Generalized Linear Model GLM probabilistic neural model previously considered within computational neuroscience literature Conventional classification rule SNNs operate offline based number output spike output neuron contrast novel training method proposed firsttospike decoding rule whereby SNN perform early classification decision spike firing detected output neuron Numerical result bring insight optimal parameter selection GLM neuron accuracycomplexity tradeoff performance conventional firsttospike decoding Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Qiang Li', 'Yan He', 'Jing-ping Jiang']

0812.0743v2

Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.

A Novel Clustering Algorithm Based on Quantum Games

http://arxiv.org/pdf/0812.0743v2

Title Novel Clustering Algorithm Based Quantum Games Summary Enormous success made quantum algorithm last decade paper combine quantum game problem data clustering develop quantumgamebased clustering algorithm data point dataset considered player make decision implement quantum strategy quantum game round quantum game player expected payoff calculated Later us linkremovingandrewiring LRR function change neighbor adjust strength link connecting order maximize payoff algorithm discussed analyzed two case strategy two payoff matrix two LRR function Consequently simulation result demonstrated data point datasets clustered reasonably efficiently clustering algorithm fast rate convergence Moreover comparison algorithm also provides indication effectiveness proposed approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andrew M. Saxe', 'James L. McClelland', 'Surya Ganguli']

1312.6120v3

Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks. Despite the linearity of their input-output map, such networks have nonlinear gradient descent dynamics on weights that change with the addition of each new hidden layer. We show that deep linear networks exhibit nonlinear learning phenomena similar to those seen in simulations of nonlinear networks, including long plateaus followed by rapid transitions to lower error solutions, and faster convergence from greedy unsupervised pretraining initial conditions than from random initial conditions. We provide an analytical description of these phenomena by finding new exact solutions to the nonlinear dynamics of deep learning. Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed can nevertheless remain finite: for a special class of initial conditions on the weights, very deep networks incur only a finite, depth independent, delay in learning speed relative to shallow networks. We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, while scaled random Gaussian initializations cannot. We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times. We further show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate in a special regime known as the edge of chaos.

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

http://arxiv.org/pdf/1312.6120v3

Title Exact solution nonlinear dynamic learning deep linear neural network Summary Despite widespread practical success deep learning method theoretical understanding dynamic learning deep neural network remains quite sparse attempt bridge gap theory practice deep learning systematically analyzing learning dynamic restricted case deep linear neural network Despite linearity inputoutput map network nonlinear gradient descent dynamic weight change addition new hidden layer show deep linear network exhibit nonlinear learning phenomenon similar seen simulation nonlinear network including long plateau followed rapid transition lower error solution faster convergence greedy unsupervised pretraining initial condition random initial condition provide analytical description phenomenon finding new exact solution nonlinear dynamic deep learning theoretical analysis also reveals surprising finding depth network approach infinity learning speed nevertheless remain finite special class initial condition weight deep network incur finite depth independent delay learning speed relative shallow network show certain condition training data unsupervised pretraining find special class initial condition scaled random Gaussian initialization cannot exhibit new class random orthogonal initial condition weight like unsupervised pretraining enjoys depth independent learning time show initial condition also lead faithful propagation gradient even deep nonlinear network long operate special regime known edge chaos Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Paul Honeine']

1411.0161v1

In signal analysis and synthesis, linear approximation theory considers a linear decomposition of any given signal in a set of atoms, collected into a so-called dictionary. Relevant sparse representations are obtained by relaxing the orthogonality condition of the atoms, yielding overcomplete dictionaries with an extended number of atoms. More generally than the linear decomposition, overcomplete kernel dictionaries provide an elegant nonlinear extension by defining the atoms through a mapping kernel function (e.g., the gaussian kernel). Models based on such kernel dictionaries are used in neural networks, gaussian processes and online learning with kernels. The quality of an overcomplete dictionary is evaluated with a diversity measure the distance, the approximation, the coherence and the Babel measures. In this paper, we develop a framework to examine overcomplete kernel dictionaries with the entropy from information theory. Indeed, a higher value of the entropy is associated to a further uniform spread of the atoms over the space. For each of the aforementioned diversity measures, we derive lower bounds on the entropy. Several definitions of the entropy are examined, with an extensive analysis in both the input space and the mapped feature space.

Entropy of Overcomplete Kernel Dictionaries

http://arxiv.org/pdf/1411.0161v1

Title Entropy Overcomplete Kernel Dictionaries Summary signal analysis synthesis linear approximation theory considers linear decomposition given signal set atom collected socalled dictionary Relevant sparse representation obtained relaxing orthogonality condition atom yielding overcomplete dictionary extended number atom generally linear decomposition overcomplete kernel dictionary provide elegant nonlinear extension defining atom mapping kernel function eg gaussian kernel Models based kernel dictionary used neural network gaussian process online learning kernel quality overcomplete dictionary evaluated diversity measure distance approximation coherence Babel measure paper develop framework examine overcomplete kernel dictionary entropy information theory Indeed higher value entropy associated uniform spread atom space aforementioned diversity measure derive lower bound entropy Several definition entropy examined extensive analysis input space mapped feature space Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sander Dieleman', 'Kyle W. Willett', 'Joni Dambre']

1503.07077v1

Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological analysis has traditionally been carried out mostly via visual inspection by trained experts, which is time-consuming and does not scale to large ($\gtrsim10^4$) numbers of images. Although attempts have been made to build automated classification systems, these have not been able to achieve the desired level of accuracy. The Galaxy Zoo project successfully applied a crowdsourcing strategy, inviting online users to classify images by answering a series of questions. Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images. We present a deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry. It was developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project. For images with high agreement among the Galaxy Zoo participants, our model is able to reproduce their consensus with near-perfect accuracy ($> 99\%$) for most questions. Confident model predictions are highly accurate, which makes the model suitable for filtering large collections of images and forwarding challenging images to experts for manual annotation. This approach greatly reduces the experts' workload without affecting accuracy. The application of these algorithms to larger sets of training data will be critical for analysing results from future surveys such as the LSST.

Rotation-invariant convolutional neural networks for galaxy morphology prediction

http://arxiv.org/pdf/1503.07077v1

Title Rotationinvariant convolutional neural network galaxy morphology prediction Summary Measuring morphological parameter galaxy key requirement studying formation evolution Surveys Sloan Digital Sky Survey SDSS resulted availability large collection image permitted populationwide analysis galaxy morphology Morphological analysis traditionally carried mostly via visual inspection trained expert timeconsuming scale large gtrsim104 number image Although attempt made build automated classification system able achieve desired level accuracy Galaxy Zoo project successfully applied crowdsourcing strategy inviting online user classify image answering series question Unfortunately even approach scale well enough keep increasing availability galaxy image present deep neural network model galaxy morphology classification exploit translational rotational symmetry developed context Galaxy Challenge international competition build best model morphology classification based annotated image Galaxy Zoo project image high agreement among Galaxy Zoo participant model able reproduce consensus nearperfect accuracy 99 question Confident model prediction highly accurate make model suitable filtering large collection image forwarding challenging image expert manual annotation approach greatly reduces expert workload without affecting accuracy application algorithm larger set training data critical analysing result future survey LSST Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Fei Zhu', 'Paul Honeine', 'Maya Kallas']

1407.4420v2

The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in remote sensing. A great challenge arises when dealing with a nonlinear formulation of the NMF. Within the framework of kernel machines, the models suggested in the literature do not allow the representation of the factorization matrices, which is a fallout of the curse of the pre-image. In this paper, we propose a novel kernel-based model for the NMF that does not suffer from the pre-image problem, by investigating the estimation of the factorization matrices directly in the input space. For different kernel functions, we describe two schemes for iterative algorithms: an additive update rule based on a gradient descent scheme and a multiplicative update rule in the same spirit as in the Lee and Seung algorithm. Within the proposed framework, we develop several extensions to incorporate constraints, including sparseness, smoothness, and spatial regularization with a total-variation-like penalty. The effectiveness of the proposed method is demonstrated with the problem of unmixing hyperspectral images, using well-known real images and results with state-of-the-art techniques.

Kernel Nonnegative Matrix Factorization Without the Curse of the Pre-image - Application to Unmixing Hyperspectral Images

http://arxiv.org/pdf/1407.4420v2

Title Kernel Nonnegative Matrix Factorization Without Curse Preimage Application Unmixing Hyperspectral Images Summary nonnegative matrix factorization NMF widely used signal image processing including bioinformatics blind source separation hyperspectral image analysis remote sensing great challenge arises dealing nonlinear formulation NMF Within framework kernel machine model suggested literature allow representation factorization matrix fallout curse preimage paper propose novel kernelbased model NMF suffer preimage problem investigating estimation factorization matrix directly input space different kernel function describe two scheme iterative algorithm additive update rule based gradient descent scheme multiplicative update rule spirit Lee Seung algorithm Within proposed framework develop several extension incorporate constraint including sparseness smoothness spatial regularization totalvariationlike penalty effectiveness proposed method demonstrated problem unmixing hyperspectral image using wellknown real image result stateoftheart technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Paul Honeine']

1409.6046v1

Many machine learning frameworks, such as resource-allocating networks, kernel-based methods, Gaussian processes, and radial-basis-function networks, require a sparsification scheme in order to address the online learning paradigm. For this purpose, several online sparsification criteria have been proposed to restrict the model definition on a subset of samples. The most known criterion is the (linear) approximation criterion, which discards any sample that can be well represented by the already contributing samples, an operation with excessive computational complexity. Several computationally efficient sparsification criteria have been introduced in the literature, such as the distance, the coherence and the Babel criteria. In this paper, we provide a framework that connects these sparsification criteria to the issue of approximating samples, by deriving theoretical bounds on the approximation errors. Moreover, we investigate the error of approximating any feature, by proposing upper-bounds on the approximation error for each of the aforementioned sparsification criteria. Two classes of features are described in detail, the empirical mean and the principal axes in the kernel principal component analysis.

Approximation errors of online sparsification criteria

http://arxiv.org/pdf/1409.6046v1

Title Approximation error online sparsification criterion Summary Many machine learning framework resourceallocating network kernelbased method Gaussian process radialbasisfunction network require sparsification scheme order address online learning paradigm purpose several online sparsification criterion proposed restrict model definition subset sample known criterion linear approximation criterion discard sample well represented already contributing sample operation excessive computational complexity Several computationally efficient sparsification criterion introduced literature distance coherence Babel criterion paper provide framework connects sparsification criterion issue approximating sample deriving theoretical bound approximation error Moreover investigate error approximating feature proposing upperbounds approximation error aforementioned sparsification criterion Two class feature described detail empirical mean principal ax kernel principal component analysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Thomas Wiatowski', 'Michael Tschannen', 'Aleksandar Stanić', 'Philipp Grohs', 'Helmut Bölcskei']

1605.08283v1

First steps towards a mathematical theory of deep convolutional neural networks for feature extraction were made---for the continuous-time case---in Mallat, 2012, and Wiatowski and B\"olcskei, 2015. This paper considers the discrete case, introduces new convolutional neural network architectures, and proposes a mathematical framework for their analysis. Specifically, we establish deformation and translation sensitivity results of local and global nature, and we investigate how certain structural properties of the input signal are reflected in the corresponding feature vectors. Our theory applies to general filters and general Lipschitz-continuous non-linearities and pooling operators. Experiments on handwritten digit classification and facial landmark detection---including feature importance evaluation---complement the theoretical findings.

Discrete Deep Feature Extraction: A Theory and New Architectures

http://arxiv.org/pdf/1605.08283v1

Title Discrete Deep Feature Extraction Theory New Architectures Summary First step towards mathematical theory deep convolutional neural network feature extraction madefor continuoustime casein Mallat 2012 Wiatowski Bolcskei 2015 paper considers discrete case introduces new convolutional neural network architecture proposes mathematical framework analysis Specifically establish deformation translation sensitivity result local global nature investigate certain structural property input signal reflected corresponding feature vector theory applies general filter general Lipschitzcontinuous nonlinearities pooling operator Experiments handwritten digit classification facial landmark detectionincluding feature importance evaluationcomplement theoretical finding Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lifeng Shang', 'Zhengdong Lu', 'Hang Li']

1503.02364v2

We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large amount of one-round conversation data collected from a microblogging service. Empirical study shows that NRM can generate grammatically correct and content-wise appropriate responses to over 75% of the input text, outperforming state-of-the-arts in the same setting, including retrieval-based and SMT-based models.

Neural Responding Machine for Short-Text Conversation

http://arxiv.org/pdf/1503.02364v2

Title Neural Responding Machine ShortText Conversation Summary propose Neural Responding Machine NRM neural networkbased response generator ShortText Conversation NRM take general encoderdecoder framework formalizes generation response decoding process based latent representation input text encoding decoding realized recurrent neural network RNN NRM trained large amount oneround conversation data collected microblogging service Empirical study show NRM generate grammatically correct contentwise appropriate response 75 input text outperforming stateofthearts setting including retrievalbased SMTbased model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nabiha Asghar', 'Pascal Poupart', 'Xin Jiang', 'Hang Li']

1612.03929v5

We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.

Deep Active Learning for Dialogue Generation

http://arxiv.org/pdf/1612.03929v5

Title Deep Active Learning Dialogue Generation Summary propose online endtoend neural generative conversational model opendomain dialogue trained using unique combination offline twophase supervised learning online humanintheloop active learning existing research proposes offline supervision handcrafted reward function online reinforcement devise novel interactive learning mechanism based hammingdiverse beam search response generation onecharacter userfeedback step Experiments show model inherently promotes generation semantically relevant interesting response used train agent customized persona mood conversational style Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Karl Moritz Hermann', 'Tomáš Kočiský', 'Edward Grefenstette', 'Lasse Espeholt', 'Will Kay', 'Mustafa Suleyman', 'Phil Blunsom']

1506.03340v3

Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.

Teaching Machines to Read and Comprehend

http://arxiv.org/pdf/1506.03340v3

Title Teaching Machines Read Comprehend Summary Teaching machine read natural language document remains elusive challenge Machine reading system tested ability answer question posed content document seen large scale training test datasets missing type evaluation work define new methodology resolve bottleneck provides large scale supervised reading comprehension data allows u develop class attention based deep neural network learn read real document answer complex question minimal prior knowledge language structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jianpeng Cheng', 'Dimitri Kartsaklis']

1508.02354v2

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.

Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning

http://arxiv.org/pdf/1508.02354v2

Title SyntaxAware MultiSense Word Embeddings Deep Compositional Models Meaning Summary Deep compositional model meaning acting distributional representation word order produce vector larger text constituent evolving popular area NLP research detail compositional distributional framework based rich form word embeddings aim facilitating interaction word context sentence Embeddings composition layer jointly learned generic objective enhances vector syntactic information surrounding context Furthermore word associated number sens plausible selected dynamically composition process evaluate produced vector qualitatively quantitatively positive result sentence level effectiveness framework demonstrated MSRPar task report result within stateoftheart range Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shengxian Wan', 'Yanyan Lan', 'Jiafeng Guo', 'Jun Xu', 'Liang Pang', 'Xueqi Cheng']

1511.08277v1

Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations. Specifically, each positional sentence representation is a sentence representation at this position, generated by a bidirectional long short term memory (Bi-LSTM). The matching score is finally produced by aggregating interactions between these different positional sentence representations, through $k$-Max pooling and a multi-layer perceptron. Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

http://arxiv.org/pdf/1511.08277v1

Title Deep Architecture Semantic Matching Multiple Positional Sentence Representations Summary Matching natural language sentence central many application information retrieval question answering Existing deep model rely single sentence representation multiple granularity representation matching However method cannot well capture contextualized local information matching process tackle problem present new deep architecture match two sentence multiple positional sentence representation Specifically positional sentence representation sentence representation position generated bidirectional long short term memory BiLSTM matching score finally produced aggregating interaction different positional sentence representation kMax pooling multilayer perceptron model several advantage 1 using BiLSTM rich context whole sentence leveraged capture contextualized local information positional sentence representation 2 matching multiple positional sentence representation flexible aggregate different important contextualized local information sentence support matching 3 Experiments different task question answering sentence completion demonstrate superiority model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yiming Cui', 'Shijin Wang', 'Jianfeng Li']

1512.00177v3

Artificial neural networks are powerful models, which have been widely applied into many aspects of machine translation, such as language modeling and translation modeling. Though notable improvements have been made in these areas, the reordering problem still remains a challenge in statistical machine translations. In this paper, we present a novel neural reordering model that directly models word pairs and alignment. By utilizing LSTM recurrent neural networks, much longer context could be learned for reordering prediction. Experimental results on NIST OpenMT12 Arabic-English and Chinese-English 1000-best rescoring task show that our LSTM neural reordering feature is robust and achieves significant improvements over various baseline systems.

LSTM Neural Reordering Feature for Statistical Machine Translation

http://arxiv.org/pdf/1512.00177v3

Title LSTM Neural Reordering Feature Statistical Machine Translation Summary Artificial neural network powerful model widely applied many aspect machine translation language modeling translation modeling Though notable improvement made area reordering problem still remains challenge statistical machine translation paper present novel neural reordering model directly model word pair alignment utilizing LSTM recurrent neural network much longer context could learned reordering prediction Experimental result NIST OpenMT12 ArabicEnglish ChineseEnglish 1000best rescoring task show LSTM neural reordering feature robust achieves significant improvement various baseline system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shuohang Wang', 'Jing Jiang']

1512.08849v2

Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI). In this paper, we propose a special long short-term memory (LSTM) architecture for NLI. Our model builds on top of a recently proposed neural attention model for NLI but is based on a significantly different idea. Instead of deriving sentence embeddings for the premise and the hypothesis to be used for classification, our solution uses a match-LSTM to perform word-by-word matching of the hypothesis with the premise. This LSTM is able to place more emphasis on important word-level matching results. In particular, we observe that this LSTM remembers important mismatches that are critical for predicting the contradiction or the neutral relationship label. On the SNLI corpus, our model achieves an accuracy of 86.1%, outperforming the state of the art.

Learning Natural Language Inference with LSTM

http://arxiv.org/pdf/1512.08849v2

Title Learning Natural Language Inference LSTM Summary Natural language inference NLI fundamentally important task natural language processing many application recently released Stanford Natural Language Inference SNLI corpus made possible develop evaluate learningcentered method deep neural network natural language inference NLI paper propose special long shortterm memory LSTM architecture NLI model build top recently proposed neural attention model NLI based significantly different idea Instead deriving sentence embeddings premise hypothesis used classification solution us matchLSTM perform wordbyword matching hypothesis premise LSTM able place emphasis important wordlevel matching result particular observe LSTM remembers important mismatch critical predicting contradiction neutral relationship label SNLI corpus model achieves accuracy 861 outperforming state art Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Phong Le', 'Willem Zuidema']

1603.00423v1

Recursive neural networks (RNN) and their recently proposed extension recursive long short term memory networks (RLSTM) are models that compute representations for sentences, by recursively combining word embeddings according to an externally provided parse tree. Both models thus, unlike recurrent networks, explicitly make use of the hierarchical structure of a sentence. In this paper, we demonstrate that RNNs nevertheless suffer from the vanishing gradient and long distance dependency problem, and that RLSTMs greatly improve over RNN's on these problems. We present an artificial learning task that allows us to quantify the severity of these problems for both models. We further show that a ratio of gradients (at the root node and a focal leaf node) is highly indicative of the success of backpropagation at optimizing the relevant weights low in the tree. This paper thus provides an explanation for existing, superior results of RLSTMs on tasks such as sentiment analysis, and suggests that the benefits of including hierarchical structure and of including LSTM-style gating are complementary.

Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs

http://arxiv.org/pdf/1603.00423v1

Title Quantifying vanishing gradient long distance dependency problem recursive neural network recursive LSTMs Summary Recursive neural network RNN recently proposed extension recursive long short term memory network RLSTM model compute representation sentence recursively combining word embeddings according externally provided parse tree model thus unlike recurrent network explicitly make use hierarchical structure sentence paper demonstrate RNNs nevertheless suffer vanishing gradient long distance dependency problem RLSTMs greatly improve RNNs problem present artificial learning task allows u quantify severity problem model show ratio gradient root node focal leaf node highly indicative success backpropagation optimizing relevant weight low tree paper thus provides explanation existing superior result RLSTMs task sentiment analysis suggests benefit including hierarchical structure including LSTMstyle gating complementary Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yang Liu', 'Sujian Li', 'Xiaodong Zhang', 'Zhifang Sui']

1603.02776v1

Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.

Implicit Discourse Relation Classification via Multi-Task Neural Networks

http://arxiv.org/pdf/1603.02776v1

Title Implicit Discourse Relation Classification via MultiTask Neural Networks Summary Without discourse connective classifying implicit discourse relation challenging task bottleneck building practical discourse parser Previous research usually make use one kind discourse framework PDTB RST improve classification performance discourse relation Actually different discourse annotation framework exist multiple corpus internal connection exploit combination different discourse corpus design related discourse classification task specific corpus propose novel Convolutional Neural Network embedded multitask learning system synthesize task learning unique shared representation task experimental result PDTB implicit discourse relation classification task demonstrate model achieves significant gain baseline system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Peng Li', 'Heng Huang']

1603.09405v1

Neural network based approaches for sentence relation modeling automatically generate hidden matching features from raw sentence pairs. However, the quality of matching feature representation may not be satisfied due to complex semantic relations such as entailment or contradiction. To address this challenge, we propose a new deep neural network architecture that jointly leverage pre-trained word embedding and auxiliary character embedding to learn sentence meanings. The two kinds of word sequence representations as inputs into multi-layer bidirectional LSTM to learn enhanced sentence representation. After that, we construct matching features followed by another temporal CNN to learn high-level hidden matching feature representations. Experimental results demonstrate that our approach consistently outperforms the existing methods on standard evaluation datasets.

Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding

http://arxiv.org/pdf/1603.09405v1

Title Enhancing Sentence Relation Modeling Auxiliary Characterlevel Embedding Summary Neural network based approach sentence relation modeling automatically generate hidden matching feature raw sentence pair However quality matching feature representation may satisfied due complex semantic relation entailment contradiction address challenge propose new deep neural network architecture jointly leverage pretrained word embedding auxiliary character embedding learn sentence meaning two kind word sequence representation input multilayer bidirectional LSTM learn enhanced sentence representation construct matching feature followed another temporal CNN learn highlevel hidden matching feature representation Experimental result demonstrate approach consistently outperforms existing method standard evaluation datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Byungsoo Kim', 'Hwanjo Yu', 'Gary Geunbae Lee']

1605.07918v1

Previous studies in Open Information Extraction (Open IE) are mainly based on extraction patterns. They manually define patterns or automatically learn them from a large corpus. However, these approaches are limited when grasping the context of a sentence, and they fail to capture implicit relations. In this paper, we address this problem with the following methods. First, we exploit long short-term memory (LSTM) networks to extract higher-level features along the shortest dependency paths, connecting headwords of relations and arguments. The path-level features from LSTM networks provide useful clues regarding contextual information and the validity of arguments. Second, we constructed samples to train LSTM networks without the need for manual labeling. In particular, feedback negative sampling picks highly negative samples among non-positive samples through a model trained with positive samples. The experimental results show that our approach produces more precise and abundant extractions than state-of-the-art open IE systems. To the best of our knowledge, this is the first work to apply deep learning to Open IE.

Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks with Feedback Negative Sampling

http://arxiv.org/pdf/1605.07918v1

Title Automatic Open Knowledge Acquisition via Long ShortTerm Memory Networks Feedback Negative Sampling Summary Previous study Open Information Extraction Open IE mainly based extraction pattern manually define pattern automatically learn large corpus However approach limited grasping context sentence fail capture implicit relation paper address problem following method First exploit long shortterm memory LSTM network extract higherlevel feature along shortest dependency path connecting headword relation argument pathlevel feature LSTM network provide useful clue regarding contextual information validity argument Second constructed sample train LSTM network without need manual labeling particular feedback negative sampling pick highly negative sample among nonpositive sample model trained positive sample experimental result show approach produce precise abundant extraction stateoftheart open IE system best knowledge first work apply deep learning Open IE Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yuanzhe Zhang', 'Kang Liu', 'Shizhu He', 'Guoliang Ji', 'Zhanyi Liu', 'Hua Wu', 'Jun Zhao']

1606.00979v1

With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Knowledge base-based question answering (KB-QA) is one of the most promising approaches to access the substantial knowledge. Meantime, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is unable to express the proper information of the question. Hence, we present a neural attention-based model to represent the questions dynamically according to the different focuses of various candidate answer aspects. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. And it also alleviates the out of vocabulary (OOV) problem, which helps the attention model to represent the question more precisely. The experimental results on WEBQUESTIONS demonstrate the effectiveness of the proposed approach.

Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information

http://arxiv.org/pdf/1606.00979v1

Title Question Answering Knowledge Base Neural Attention Combining Global Knowledge Information Summary rapid growth knowledge base KBs web take full advantage becomes increasingly important Knowledge basebased question answering KBQA one promising approach access substantial knowledge Meantime neural networkbased NNbased method develop NNbased KBQA already achieved impressive result However previous work put emphasis question representation question converted fixed vector regardless candidate answer simple representation strategy unable express proper information question Hence present neural attentionbased model represent question dynamically according different focus various candidate answer aspect addition leverage global knowledge inside underlying KB aiming integrating rich KB information representation answer also alleviates vocabulary OOV problem help attention model represent question precisely experimental result WEBQUESTIONS demonstrate effectiveness proposed approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vladyslav Kolesnyk', 'Tim Rocktäschel', 'Sebastian Riedel']

1606.01404v1

The ability to reason with natural language is a fundamental prerequisite for many NLP tasks such as information extraction, machine translation and question answering. To quantify this ability, systems are commonly tested whether they can recognize textual entailment, i.e., whether one sentence can be inferred from another one. However, in most NLP applications only single source sentences instead of sentence pairs are available. Hence, we propose a new task that measures how well a model can generate an entailed sentence from a source sentence. We take entailment-pairs of the Stanford Natural Language Inference corpus and train an LSTM with attention. On a manually annotated test set we found that 82% of generated sentences are correct, an improvement of 10.3% over an LSTM baseline. A qualitative analysis shows that this model is not only capable of shortening input sentences, but also inferring new statements via paraphrasing and phrase entailment. We then apply this model recursively to input-output pairs, thereby generating natural language inference chains that can be used to automatically construct an entailment graph from source sentences. Finally, by swapping source and target sentences we can also train a model that given an input sentence invents additional information to generate a new sentence.

Generating Natural Language Inference Chains

http://arxiv.org/pdf/1606.01404v1

Title Generating Natural Language Inference Chains Summary ability reason natural language fundamental prerequisite many NLP task information extraction machine translation question answering quantify ability system commonly tested whether recognize textual entailment ie whether one sentence inferred another one However NLP application single source sentence instead sentence pair available Hence propose new task measure well model generate entailed sentence source sentence take entailmentpairs Stanford Natural Language Inference corpus train LSTM attention manually annotated test set found 82 generated sentence correct improvement 103 LSTM baseline qualitative analysis show model capable shortening input sentence also inferring new statement via paraphrasing phrase entailment apply model recursively inputoutput pair thereby generating natural language inference chain used automatically construct entailment graph source sentence Finally swapping source target sentence also train model given input sentence invents additional information generate new sentence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dirk Weissenborn', 'Tim Rocktäschel']

1606.03002v1

Recurrent neural networks such as the GRU and LSTM found wide adoption in natural language processing and achieve state-of-the-art results for many tasks. These models are characterized by a memory state that can be written to and read from by applying gated composition operations to the current input and the previous state. However, they only cover a small subset of potentially useful compositions. We propose Multi-Function Recurrent Units (MuFuRUs) that allow for arbitrary differentiable functions as composition operations. Furthermore, MuFuRUs allow for an input- and state-dependent choice of these composition operations that is learned. Our experiments demonstrate that the additional functionality helps in different sequence modeling tasks, including the evaluation of propositional logic formulae, language modeling and sentiment analysis.

MuFuRU: The Multi-Function Recurrent Unit

http://arxiv.org/pdf/1606.03002v1

Title MuFuRU MultiFunction Recurrent Unit Summary Recurrent neural network GRU LSTM found wide adoption natural language processing achieve stateoftheart result many task model characterized memory state written read applying gated composition operation current input previous state However cover small subset potentially useful composition propose MultiFunction Recurrent Units MuFuRUs allow arbitrary differentiable function composition operation Furthermore MuFuRUs allow input statedependent choice composition operation learned experiment demonstrate additional functionality help different sequence modeling task including evaluation propositional logic formula language modeling sentiment analysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hendrik Strobelt', 'Sebastian Gehrmann', 'Hanspeter Pfister', 'Alexander M. Rush']

1606.07461v2

Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVIS, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows users to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We show several use cases of the tool for analyzing specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis. We characterize the domain, the different stakeholders, and their goals and tasks.

LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks

http://arxiv.org/pdf/1606.07461v2

Title LSTMVis Tool Visual Analysis Hidden State Dynamics Recurrent Neural Networks Summary Recurrent neural network particular long shortterm memory LSTM network remarkably effective tool sequence modeling learn dense blackbox hidden representation sequential input Researchers interested better understanding model studied change hidden state representation time noticed interpretable pattern also significant noise work present LSTMVIS visual analysis tool recurrent neural network focus understanding hidden state dynamic tool allows user select hypothesis input range focus local state change match state change similar pattern large data set align result structural annotation domain show several use case tool analyzing specific hidden state property dataset containing nesting phrase structure chord progression demonstrate tool used isolate pattern statistical analysis characterize domain different stakeholder goal task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Abigail See', 'Minh-Thang Luong', 'Christopher D. Manning']

1606.09274v1

Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes. This paper examines three simple magnitude-based pruning schemes to compress NMT models, namely class-blind, class-uniform, and class-distribution, which differ in terms of how pruning thresholds are computed for the different classes of weights in the NMT architecture. We demonstrate the efficacy of weight pruning as a compression technique for a state-of-the-art NMT system. We show that an NMT model with over 200 million parameters can be pruned by 40% with very little performance loss as measured on the WMT'14 English-German translation task. This sheds light on the distribution of redundancy in the NMT architecture. Our main result is that with retraining, we can recover and even surpass the original performance with an 80%-pruned model.

Compression of Neural Machine Translation Models via Pruning

http://arxiv.org/pdf/1606.09274v1

Title Compression Neural Machine Translation Models via Pruning Summary Neural Machine Translation NMT like many deep learning domain typically suffers overparameterization resulting large storage size paper examines three simple magnitudebased pruning scheme compress NMT model namely classblind classuniform classdistribution differ term pruning threshold computed different class weight NMT architecture demonstrate efficacy weight pruning compression technique stateoftheart NMT system show NMT model 200 million parameter pruned 40 little performance loss measured WMT14 EnglishGerman translation task shed light distribution redundancy NMT architecture main result retraining recover even surpass original performance 80pruned model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Janez Starc', 'Dunja Mladenić']

1607.06025v2

Natural Language Inference is an important task for Natural Language Understanding. It is concerned with classifying the logical relation between two sentences. In this paper, we propose several text generative neural networks for generating text hypothesis, which allows construction of new Natural Language Inference datasets. To evaluate the models, we propose a new metric -- the accuracy of the classifier trained on the generated dataset. The accuracy obtained by our best generative model is only 2.7% lower than the accuracy of the classifier trained on the original, human crafted dataset. Furthermore, the best generated dataset combined with the original dataset achieves the highest accuracy. The best model learns a mapping embedding for each training example. By comparing various metrics we show that datasets that obtain higher ROUGE or METEOR scores do not necessarily yield higher classification accuracies. We also provide analysis of what are the characteristics of a good dataset including the distinguishability of the generated datasets from the original one.

Constructing a Natural Language Inference Dataset using Generative Neural Networks

http://arxiv.org/pdf/1607.06025v2

Title Constructing Natural Language Inference Dataset using Generative Neural Networks Summary Natural Language Inference important task Natural Language Understanding concerned classifying logical relation two sentence paper propose several text generative neural network generating text hypothesis allows construction new Natural Language Inference datasets evaluate model propose new metric accuracy classifier trained generated dataset accuracy obtained best generative model 27 lower accuracy classifier trained original human crafted dataset Furthermore best generated dataset combined original dataset achieves highest accuracy best model learns mapping embedding training example comparing various metric show datasets obtain higher ROUGE METEOR score necessarily yield higher classification accuracy also provide analysis characteristic good dataset including distinguishability generated datasets original one Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Peng Li', 'Wei Li', 'Zhengyan He', 'Xuguang Wang', 'Ying Cao', 'Jie Zhou', 'Wei Xu']

1607.06275v2

While question answering (QA) with neural network, i.e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system. To alleviate this problem, we propose a large scale human annotated real-world QA dataset WebQA with more than 42k questions and 556k evidences. As existing neural QA methods resolve QA either as sequence generation or classification/ranking problem, they face challenges of expensive softmax computation, unseen answers handling or separate candidate answer generation component. In this work, we cast neural QA as a sequence labeling problem and propose an end-to-end sequence labeling model, which overcomes all the above challenges. Experimental results on WebQA show that our model outperforms the baselines significantly with an F1 score of 74.69% with word-based input, and the performance drops only 3.72 F1 points with more challenging character-based input.

Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

http://arxiv.org/pdf/1607.06275v2

Title Dataset Neural Recurrent Sequence Labeling Model OpenDomain Factoid Question Answering Summary question answering QA neural network ie neural QA achieved promising result recent year lacking large scale realword QA dataset still challenge developing evaluating neural QA system alleviate problem propose large scale human annotated realworld QA dataset WebQA 42k question 556k evidence existing neural QA method resolve QA either sequence generation classificationranking problem face challenge expensive softmax computation unseen answer handling separate candidate answer generation component work cast neural QA sequence labeling problem propose endtoend sequence labeling model overcomes challenge Experimental result WebQA show model outperforms baseline significantly F1 score 7469 wordbased input performance drop 372 F1 point challenging characterbased input Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Soroush Vosoughi', 'Prashanth Vijayaraghavan', 'Deb Roy']

1607.07514v1

We present Tweet2Vec, a novel method for generating general-purpose vector representation of tweets. The model learns tweet embeddings using character-level CNN-LSTM encoder-decoder. We trained our model on 3 million, randomly selected English-language tweets. The model was evaluated using two methods: tweet semantic similarity and tweet sentiment categorization, outperforming the previous state-of-the-art in both tasks. The evaluations demonstrate the power of the tweet embeddings generated by our model for various tweet categorization tasks. The vector representations generated by our model are generic, and hence can be applied to a variety of tasks. Though the model presented in this paper is trained on English-language tweets, the method presented can be used to learn tweet embeddings for different languages.

Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

http://arxiv.org/pdf/1607.07514v1

Title Tweet2Vec Learning Tweet Embeddings Using Characterlevel CNNLSTM EncoderDecoder Summary present Tweet2Vec novel method generating generalpurpose vector representation tweet model learns tweet embeddings using characterlevel CNNLSTM encoderdecoder trained model 3 million randomly selected Englishlanguage tweet model evaluated using two method tweet semantic similarity tweet sentiment categorization outperforming previous stateoftheart task evaluation demonstrate power tweet embeddings generated model various tweet categorization task vector representation generated model generic hence applied variety task Though model presented paper trained Englishlanguage tweet method presented used learn tweet embeddings different language Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lei Yu', 'Jan Buys', 'Phil Blunsom']

1609.08194v1

We introduce an online neural sequence to sequence model that learns to alternate between encoding and decoding segments of the input as it is read. By independently tracking the encoding and decoding representations our algorithm permits exact polynomial marginalization of the latent segmentation during training, and during decoding beam search is employed to find the best alignment path together with the predicted output sequence. Our model tackles the bottleneck of vanilla encoder-decoders that have to read and memorize the entire input sequence in their fixed-length hidden states before producing any output. It is different from previous attentive models in that, instead of treating the attention weights as output of a deterministic function, our model assigns attention weights to a sequential latent variable which can be marginalized out and permits online generation. Experiments on abstractive sentence summarization and morphological inflection show significant performance gains over the baseline encoder-decoders.

Online Segment to Segment Neural Transduction

http://arxiv.org/pdf/1609.08194v1

Title Online Segment Segment Neural Transduction Summary introduce online neural sequence sequence model learns alternate encoding decoding segment input read independently tracking encoding decoding representation algorithm permit exact polynomial marginalization latent segmentation training decoding beam search employed find best alignment path together predicted output sequence model tackle bottleneck vanilla encoderdecoders read memorize entire input sequence fixedlength hidden state producing output different previous attentive model instead treating attention weight output deterministic function model assigns attention weight sequential latent variable marginalized permit online generation Experiments abstractive sentence summarization morphological inflection show significant performance gain baseline encoderdecoders Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tomáš Kočiský', 'Gábor Melis', 'Edward Grefenstette', 'Chris Dyer', 'Wang Ling', 'Phil Blunsom', 'Karl Moritz Hermann']

1609.09315v1

We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing. The unsupervised component is based on a generative model in which latent sentences generate the unpaired logical forms. We apply this method to a number of semantic parsing tasks focusing on domains with limited access to labelled training data and extend those datasets with synthetically generated logical forms.

Semantic Parsing with Semi-Supervised Sequential Autoencoders

http://arxiv.org/pdf/1609.09315v1

Title Semantic Parsing SemiSupervised Sequential Autoencoders Summary present novel semisupervised approach sequence transduction apply semantic parsing unsupervised component based generative model latent sentence generate unpaired logical form apply method number semantic parsing task focusing domain limited access labelled training data extend datasets synthetically generated logical form Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lina M. Rojas Barahona', 'Milica Gasic', 'Nikola Mrkšić', 'Pei-Hao Su', 'Stefan Ultes', 'Tsung-Hsien Wen', 'Steve Young']

1610.04120v1

This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).

Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding

http://arxiv.org/pdf/1610.04120v1

Title Exploiting Sentence Context Representations Deep Neural Models Spoken Language Understanding Summary paper present deep learning architecture semantic decoder component Statistical Spoken Dialogue System slotfilling dialogue semantic decoder predicts dialogue act set slotvalue pair set nbest hypothesis returned Automatic Speech Recognition current model spoken language understanding assume wordaligned semantic annotation sequence tagger ii delexicalisation mapping input word domainspecific concept using heuristic try capture morphological variation scale domain language variation eg morphology synonym paraphrasing work semantic decoder trained using unaligned semantic annotation us distributed semantic representation learning overcome limitation explicit delexicalisation proposed architecture us convolutional neural network sentence representation longshort term memory network context representation Results presented publicly available DSTC2 corpus Incar corpus similar DSTC2 significantly higher word error rate WER Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lei Yu', 'Phil Blunsom', 'Chris Dyer', 'Edward Grefenstette', 'Tomas Kocisky']

1611.02554v2

We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control how much of the conditioning sequence the channel model needs to read in order to generate a subsequent symbol, we obtain a tractable and effective beam search decoder. Experimental results on abstractive sentence summarisation, morphological inflection, and machine translation show that noisy channel models outperform direct models, and that they significantly benefit from increased amounts of unpaired output data that direct models cannot easily use.

The Neural Noisy Channel

http://arxiv.org/pdf/1611.02554v2

Title Neural Noisy Channel Summary formulate sequence sequence transduction noisy channel decoding problem use recurrent neural network parameterise source channel model Unlike direct model suffer explainingaway effect training noisy channel model must produce output explain input component model trained paired training sample also unpaired sample marginal output distribution Using latent variable control much conditioning sequence channel model need read order generate subsequent symbol obtain tractable effective beam search decoder Experimental result abstractive sentence summarisation morphological inflection machine translation show noisy channel model outperform direct model significantly benefit increased amount unpaired output data direct model cannot easily use Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian Vlad Serban', 'Ryan Lowe', 'Laurent Charlin', 'Joelle Pineau']

1611.06216v1

Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising results for unstructured tasks, such as word-level dialogue response generation. The hope is that such models will be able to leverage massive amounts of data to learn meaningful natural language representations and response generation strategies, while requiring a minimum amount of domain knowledge and hand-crafting. An important challenge is to develop models that can effectively incorporate dialogue context and generate meaningful and diverse responses. In support of this goal, we review recently proposed models based on generative encoder-decoder neural network architectures, and show that these models have better ability to incorporate long-term dialogue history, to model uncertainty and ambiguity in dialogue, and to generate responses with high-level compositional structure.

Generative Deep Neural Networks for Dialogue: A Short Review

http://arxiv.org/pdf/1611.06216v1

Title Generative Deep Neural Networks Dialogue Short Review Summary Researchers recently started investigating deep neural network dialogue application particular generative sequencetosequence Seq2Seq model shown promising result unstructured task wordlevel dialogue response generation hope model able leverage massive amount data learn meaningful natural language representation response generation strategy requiring minimum amount domain knowledge handcrafting important challenge develop model effectively incorporate dialogue context generate meaningful diverse response support goal review recently proposed model based generative encoderdecoder neural network architecture show model better ability incorporate longterm dialogue history model uncertainty ambiguity dialogue generate response highlevel compositional structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Avishkar Bhoopchand', 'Tim Rocktäschel', 'Earl Barr', 'Sebastian Riedel']

1611.08307v1

To enhance developer productivity, all modern integrated development environments (IDEs) include code suggestion functionality that proposes likely next tokens at the cursor. While current IDEs work well for statically-typed languages, their reliance on type annotations means that they do not provide the same level of support for dynamic programming languages as for statically-typed languages. Moreover, suggestion engines in modern IDEs do not propose expressions or multi-statement idiomatic code. Recent work has shown that language models can improve code suggestion systems by learning from software repositories. This paper introduces a neural language model with a sparse pointer network aimed at capturing very long-range dependencies. We release a large-scale code suggestion corpus of 41M lines of Python code crawled from GitHub. On this corpus, we found standard neural language models to perform well at suggesting local phenomena, but struggle to refer to identifiers that are introduced many tokens in the past. By augmenting a neural language model with a pointer network specialized in referring to predefined classes of identifiers, we obtain a much lower perplexity and a 5 percentage points increase in accuracy for code suggestion compared to an LSTM baseline. In fact, this increase in code suggestion accuracy is due to a 13 times more accurate prediction of identifiers. Furthermore, a qualitative analysis shows this model indeed captures interesting long-range dependencies, like referring to a class member defined over 60 tokens in the past.

Learning Python Code Suggestion with a Sparse Pointer Network

http://arxiv.org/pdf/1611.08307v1

Title Learning Python Code Suggestion Sparse Pointer Network Summary enhance developer productivity modern integrated development environment IDEs include code suggestion functionality proposes likely next token cursor current IDEs work well staticallytyped language reliance type annotation mean provide level support dynamic programming language staticallytyped language Moreover suggestion engine modern IDEs propose expression multistatement idiomatic code Recent work shown language model improve code suggestion system learning software repository paper introduces neural language model sparse pointer network aimed capturing longrange dependency release largescale code suggestion corpus 41M line Python code crawled GitHub corpus found standard neural language model perform well suggesting local phenomenon struggle refer identifier introduced many token past augmenting neural language model pointer network specialized referring predefined class identifier obtain much lower perplexity 5 percentage point increase accuracy code suggestion compared LSTM baseline fact increase code suggestion accuracy due 13 time accurate prediction identifier Furthermore qualitative analysis show model indeed capture interesting longrange dependency like referring class member defined 60 token past Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Guillaume Klein', 'Yoon Kim', 'Yuntian Deng', 'Jean Senellart', 'Alexander M. Rush']

1701.02810v2

We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques.

OpenNMT: Open-Source Toolkit for Neural Machine Translation

http://arxiv.org/pdf/1701.02810v2

Title OpenNMT OpenSource Toolkit Neural Machine Translation Summary describe opensource toolkit neural machine translation NMT toolkit prioritizes efficiency modularity extensibility goal supporting NMT research model architecture feature representation source modality maintaining competitive performance reasonable training requirement toolkit consists modeling translation support well detailed pedagogical documentation underlying technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dirk Weissenborn', 'Georg Wiese', 'Laura Seiffe']

1703.04816v3

Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function that goes beyond simple bag-of-words modeling, such as recurrent neural networks. Our results show that FastQA, a system that meets these two requirements, can achieve very competitive performance compared with existing models. We argue that this surprising finding puts results of previous systems and the complexity of recent QA datasets into perspective.

Making Neural QA as Simple as Possible but not Simpler

http://arxiv.org/pdf/1703.04816v3

Title Making Neural QA Simple Possible Simpler Summary Recent development largescale question answering QA datasets triggered substantial amount research endtoend neural architecture QA Increasingly complex system conceived without comparison simpler neural baseline system would justify complexity work propose simple heuristic guide development neural baseline system extractive QA task find two ingredient necessary building highperforming neural QA system first awareness question word processing context second composition function go beyond simple bagofwords modeling recurrent neural network result show FastQA system meet two requirement achieve competitive performance compared existing model argue surprising finding put result previous system complexity recent QA datasets perspective Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Albert Gatt', 'Emiel Krahmer']

1703.09902v4

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

http://arxiv.org/pdf/1703.09902v4

Title Survey State Art Natural Language Generation Core task application evaluation Summary paper survey current state art Natural Language Generation NLG defined task generating text speech nonlinguistic input survey NLG timely view change field undergone past decade especially relation new usually datadriven method well new application NLG technology survey therefore aim give uptodate synthesis research core task NLG architecture adopted task organised b highlight number relatively recent research topic arisen partly result growing synergy NLG area artificial intelligence c draw attention challenge NLG evaluation relating similar challenge faced area Natural Language Processing emphasis different evaluation method relationship Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yaoyuan Zhang', 'Zhenxu Ye', 'Yansong Feng', 'Dongyan Zhao', 'Rui Yan']

1704.02312v1

Sentence simplification reduces semantic complexity to benefit people with language impairments. Previous simplification studies on the sentence level and word level have achieved promising results but also meet great challenges. For sentence-level studies, sentences after simplification are fluent but sometimes are not really simplified. For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification. In this paper, we propose a two-step simplification framework by combining both the word-level and the sentence-level simplifications, making use of their corresponding advantages. Based on the two-step framework, we implement a novel constrained neural generation model to simplify sentences given simplified words. The final results on Wikipedia and Simple Wikipedia aligned datasets indicate that our method yields better performance than various baselines.

A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification

http://arxiv.org/pdf/1704.02312v1

Title Constrained SequencetoSequence Neural Model Sentence Simplification Summary Sentence simplification reduces semantic complexity benefit people language impairment Previous simplification study sentence level word level achieved promising result also meet great challenge sentencelevel study sentence simplification fluent sometimes really simplified wordlevel study word simplified also potential grammar error due different usage word simplification paper propose twostep simplification framework combining wordlevel sentencelevel simplification making use corresponding advantage Based twostep framework implement novel constrained neural generation model simplify sentence given simplified word final result Wikipedia Simple Wikipedia aligned datasets indicate method yield better performance various baseline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mo Yu', 'Wenpeng Yin', 'Kazi Saidul Hasan', 'Cicero dos Santos', 'Bing Xiang', 'Bowen Zhou']

1704.06194v2

Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.

Improved Neural Relation Detection for Knowledge Base Question Answering

http://arxiv.org/pdf/1704.06194v2

Title Improved Neural Relation Detection Knowledge Base Question Answering Summary Relation detection core component many NLP application including Knowledge Base Question Answering KBQA paper propose hierarchical recurrent neural network enhanced residual learning detects KB relation given input question method us deep residual bidirectional LSTMs compare question relation name via different hierarchy abstraction Additionally propose simple KBQA system integrates entity linking proposed relation detector enable one enhance another Experimental result evidence approach achieves outstanding relation detection performance importantly help KBQA system achieve stateoftheart accuracy singlerelation SimpleQuestions multirelation WebQSP QA benchmark Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Edwin Simonnet', 'Sahar Ghannay', 'Nathalie Camelin', 'Yannick Estève', 'Renato De Mori']

1705.09515v1

This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems. In this study, the SLU task consists in automatically extracting, from ASR transcriptions , semantic concepts and concept/values pairs in a e.g touristic information system. An approach is proposed for enriching the set of semantic labels with error specific labels and by using a recently proposed neural approach based on word embeddings to compute well calibrated ASR confidence measures. Experimental results are reported showing that it is possible to decrease significantly the Concept/Value Error Rate with a state of the art system, outperforming previously published results performance on the same experimental data. It also shown that combining an SLU approach based on conditional random fields with a neural encoder/decoder attention based architecture , it is possible to effectively identifying confidence islands and uncertain semantic output segments useful for deciding appropriate error handling actions by the dialogue manager strategy .

ASR error management for improving spoken language understanding

http://arxiv.org/pdf/1705.09515v1

Title ASR error management improving spoken language understanding Summary paper address problem automatic speech recognition ASR error detection use improving spoken language understanding SLU system study SLU task consists automatically extracting ASR transcription semantic concept conceptvalues pair eg touristic information system approach proposed enriching set semantic label error specific label using recently proposed neural approach based word embeddings compute well calibrated ASR confidence measure Experimental result reported showing possible decrease significantly ConceptValue Error Rate state art system outperforming previously published result performance experimental data also shown combining SLU approach based conditional random field neural encoderdecoder attention based architecture possible effectively identifying confidence island uncertain semantic output segment useful deciding appropriate error handling action dialogue manager strategy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dirk Weissenborn', 'Tomáš Kočiský', 'Chris Dyer']

1706.02596v2

Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the task-specific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.

Dynamic Integration of Background Knowledge in Neural NLU Systems

http://arxiv.org/pdf/1706.02596v2

Title Dynamic Integration Background Knowledge Neural NLU Systems Summary Commonsense background knowledge required understand natural language neural natural language understanding NLU system requisite background knowledge indirectly acquired static corpus develop new reading architecture dynamic integration explicit background knowledge NLU model new taskagnostic reading module provides refined word representation taskspecific NLU architecture processing background knowledge form freetext statement together taskspecific input Strong performance task document question answering DQA recognizing textual entailment RTE demonstrate effectiveness flexibility approach Analysis show model learn exploit knowledge selectively semantically appropriate way Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shuai Tang', 'Hailin Jin', 'Chen Fang', 'Zhaowen Wang', 'Virginia R. de Sa']

1706.03146v1

We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn't aid our model to perform better, while it hurts the performance of the skip-thought model.

Rethinking Skip-thought: A Neighborhood based Approach

http://arxiv.org/pdf/1706.03146v1

Title Rethinking Skipthought Neighborhood based Approach Summary study skipthought model neighborhood information weak supervision specifically propose skipthought neighbor model consider adjacent sentence neighborhood train skipthought neighbor model large corpus continuous sentence evaluate trained model 7 task include semantic relatedness paraphrase detection classification benchmark quantitative comparison qualitative investigation conducted empirically show skipthought neighbor model performs well skipthought model evaluation task addition found incorporating autoencoder path model didnt aid model perform better hurt performance skipthought model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Georg Wiese', 'Dirk Weissenborn', 'Mariana Neves']

1706.03610v2

Factoid question answering (QA) has recently benefited from the development of deep learning (DL) systems. Neural network models outperform traditional approaches in domains where large datasets exist, such as SQuAD (ca. 100,000 questions) for Wikipedia articles. However, these systems have not yet been applied to QA in more specific domains, such as biomedicine, because datasets are generally too small to train a DL system from scratch. For example, the BioASQ dataset for biomedical QA comprises less then 900 factoid (single answer) and list (multiple answers) QA instances. In this work, we adapt a neural QA system trained on a large open-domain dataset (SQuAD, source) to a biomedical dataset (BioASQ, target) by employing various transfer learning techniques. Our network architecture is based on a state-of-the-art QA system, extended with biomedical word embeddings and a novel mechanism to answer list questions. In contrast to existing biomedical QA systems, our system does not rely on domain-specific ontologies, parsers or entity taggers, which are expensive to create. Despite this fact, our systems achieve state-of-the-art results on factoid questions and competitive results on list questions.

Neural Domain Adaptation for Biomedical Question Answering

http://arxiv.org/pdf/1706.03610v2

Title Neural Domain Adaptation Biomedical Question Answering Summary Factoid question answering QA recently benefited development deep learning DL system Neural network model outperform traditional approach domain large datasets exist SQuAD ca 100000 question Wikipedia article However system yet applied QA specific domain biomedicine datasets generally small train DL system scratch example BioASQ dataset biomedical QA comprises le 900 factoid single answer list multiple answer QA instance work adapt neural QA system trained large opendomain dataset SQuAD source biomedical dataset BioASQ target employing various transfer learning technique network architecture based stateoftheart QA system extended biomedical word embeddings novel mechanism answer list question contrast existing biomedical QA system system rely domainspecific ontology parser entity tagger expensive create Despite fact system achieve stateoftheart result factoid question competitive result list question Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sandeep Subramanian', 'Tong Wang', 'Xingdi Yuan', 'Saizheng Zhang', 'Adam Trischler', 'Yoshua Bengio']

1706.04560v2

We propose a two-stage neural model to tackle question generation from documents. Our model first estimates the probability that word sequences in a document compose "interesting" answers using a neural model trained on a question-answering corpus. We thus take a data-driven approach to interestingness. Predicted key phrases then act as target answers that condition a sequence-to-sequence question generation model with a copy mechanism. Empirically, our neural key phrase detection model significantly outperforms an entity-tagging baseline system and existing rule-based approaches. We demonstrate that the question generator formulates good quality natural language questions from extracted key phrases, and a human study indicates that our system's generated question-answer pairs are competitive with those of an earlier approach. We foresee our system being used in an educational setting to assess reading comprehension and also as a data augmentation technique for semi-supervised learning.

Neural Models for Key Phrase Detection and Question Generation

http://arxiv.org/pdf/1706.04560v2

Title Neural Models Key Phrase Detection Question Generation Summary propose twostage neural model tackle question generation document model first estimate probability word sequence document compose interesting answer using neural model trained questionanswering corpus thus take datadriven approach interestingness Predicted key phrase act target answer condition sequencetosequence question generation model copy mechanism Empirically neural key phrase detection model significantly outperforms entitytagging baseline system existing rulebased approach demonstrate question generator formulates good quality natural language question extracted key phrase human study indicates system generated questionanswer pair competitive earlier approach foresee system used educational setting ass reading comprehension also data augmentation technique semisupervised learning Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Georg Wiese', 'Dirk Weissenborn', 'Mariana Neves']

1706.08568v1

This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-of-the-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.

Neural Question Answering at BioASQ 5B

http://arxiv.org/pdf/1706.08568v1

Title Neural Question Answering BioASQ 5B Summary paper describes submission 2017 BioASQ challenge participated Task B Phase B concerned biomedical question answering QA focus factoid list question using extractive QA model restrict system output substring provided text snippet core system use FastQA stateoftheart neural QA system extended biomedical word embeddings changed answer layer able answer list question addition factoid question pretrained model largescale opendomain QA dataset SQuAD finetuned parameter BioASQ training set approach achieve stateoftheart result factoid question competitive result list question Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hongyu Guo']

1707.01555v1

While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence.

A Deep Network with Visual Text Composition Behavior

http://arxiv.org/pdf/1707.01555v1

Title Deep Network Visual Text Composition Behavior Summary natural language compositional stateoftheart neural model achieve compositionality still unclear propose deep network achieves competitive accuracy text classification also exhibit compositional behavior creating hierarchical representation piece text sentence lower layer network distribute layerspecific attention weight individual word contrast higher layer compose meaningful phrase clause whose length increase network get deeper fully composing sentence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mario Giulianelli']

1708.03910v1

There exist two main approaches to automatically extract affective orientation: lexicon-based and corpus-based. In this work, we argue that these two methods are compatible and show that combining them can improve the accuracy of emotion classifiers. In particular, we introduce a novel variant of the Label Propagation algorithm that is tailored to distributed word representations, we apply batch gradient descent to accelerate the optimization of label propagation and to make the optimization feasible for large graphs, and we propose a reproducible method for emotion lexicon expansion. We conclude that label propagation can expand an emotion lexicon in a meaningful way and that the expanded emotion lexicon can be leveraged to improve the accuracy of an emotion classifier.

Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddings

http://arxiv.org/pdf/1708.03910v1

Title Semisupervised emotion lexicon expansion label propagation specialized word embeddings Summary exist two main approach automatically extract affective orientation lexiconbased corpusbased work argue two method compatible show combining improve accuracy emotion classifier particular introduce novel variant Label Propagation algorithm tailored distributed word representation apply batch gradient descent accelerate optimization label propagation make optimization feasible large graph propose reproducible method emotion lexicon expansion conclude label propagation expand emotion lexicon meaningful way expanded emotion lexicon leveraged improve accuracy emotion classifier Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Elahe Rahimtoroghi', 'Jiaqi Wu', 'Ruimin Wang', 'Pranav Anand', 'Marilyn A Walker']

1708.09040v1

Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives. There is broad consensus that understanding a narrative requires identifying and tracking the goals and desires of the characters and their narrative outcomes. However, to date, there has been limited work on computational models for this problem. We introduce a new dataset, DesireDB, which includes gold-standard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context. We report experiments on tracking desire fulfillment using different methods, and show that LSTM Skip-Thought model achieves F-measure of 0.7 on our corpus.

Modelling Protagonist Goals and Desires in First-Person Narrative

http://arxiv.org/pdf/1708.09040v1

Title Modelling Protagonist Goals Desires FirstPerson Narrative Summary Many genre natural language text narratively structured testament predilection organizing experience narrative broad consensus understanding narrative requires identifying tracking goal desire character narrative outcome However date limited work computational model problem introduce new dataset DesireDB includes goldstandard label identifying statement desire textual evidence desire fulfillment annotation whether stated desire fulfilled given evidence narrative context report experiment tracking desire fulfillment using different method show LSTM SkipThought model achieves Fmeasure 07 corpus Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Felix Hill', 'Karl Moritz Hermann', 'Phil Blunsom', 'Stephen Clark']

1710.09867v1

Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-studied learning challenges that are also fundamental to infants learning their first words. While it is notable that models with no meaningful prior knowledge overcome these learning obstacles, AI researchers and practitioners currently lack a clear understanding of exactly how they do so. Here we address this question as a way of achieving a clearer general understanding of grounded language learning, both to inform future research and to improve confidence in model predictions. For maximum control and generality, we focus on a simple neural network-based language learning agent trained via policy-gradient methods to interpret synthetic linguistic instructions in a simulated 3D world. We apply experimental paradigms from developmental psychology to this agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel way to visualise and analyse semantic representation in grounded language learning agents that yields a plausible computational account of the observed effects.

Understanding Grounded Language Learning Agents

http://arxiv.org/pdf/1710.09867v1

Title Understanding Grounded Language Learning Agents Summary Neural networkbased system learn locate referent word phrase image answer question visual scene even execute symbolic instruction firstperson actor partiallyobservable world achieve socalled grounded language learning model must overcome certain wellstudied learning challenge also fundamental infant learning first word notable model meaningful prior knowledge overcome learning obstacle AI researcher practitioner currently lack clear understanding exactly address question way achieving clearer general understanding grounded language learning inform future research improve confidence model prediction maximum control generality focus simple neural networkbased language learning agent trained via policygradient method interpret synthetic linguistic instruction simulated 3D world apply experimental paradigm developmental psychology agent exploring condition established human bias learning effect emerge propose novel way visualise analyse semantic representation grounded language learning agent yield plausible computational account observed effect Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Anjishnu Kumar', 'Arpit Gupta', 'Julian Chan', 'Sam Tucker', 'Bjorn Hoffmeister', 'Markus Dreyer', 'Stanislav Peshterliev', 'Ankur Gandhe', 'Denis Filiminov', 'Ariya Rastrow', 'Christian Monson', 'Agnika Kumar']

1711.00549v4

This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development Kit (SDK) that enables developers to extend the capabilities of Amazon's virtual assistant, Alexa. At Amazon, the infrastructure powers over 25,000 skills deployed through the ASK, as well as AWS's Amazon Lex SLU Service. The ASK emphasizes flexibility, predictability and a rapid iteration cycle for third party developers. It imposes inductive biases that allow it to learn robust SLU models from extremely small and sparse datasets and, in doing so, removes significant barriers to entry for software developers and dialogue systems researchers.

Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding

http://arxiv.org/pdf/1711.00549v4

Title ASK Building Architecture Extensible SelfService Spoken Language Understanding Summary paper present design machine learning architecture underlies Alexa Skills Kit ASK large scale Spoken Language Understanding SLU Software Development Kit SDK enables developer extend capability Amazons virtual assistant Alexa Amazon infrastructure power 25000 skill deployed ASK well AWSs Amazon Lex SLU Service ASK emphasizes flexibility predictability rapid iteration cycle third party developer imposes inductive bias allow learn robust SLU model extremely small sparse datasets remove significant barrier entry software developer dialogue system researcher Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tomáš Kočiský', 'Jonathan Schwarz', 'Phil Blunsom', 'Chris Dyer', 'Karl Moritz Hermann', 'Gábor Melis', 'Edward Grefenstette']

1712.07040v1

Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.

The NarrativeQA Reading Comprehension Challenge

http://arxiv.org/pdf/1712.07040v1

Title NarrativeQA Reading Comprehension Challenge Summary Reading comprehension RCin contrast information retrievalrequires integrating information reasoning event entity relation across full document Question answering conventionally used ass RC ability artificial agent child learning read However existing RC datasets task dominated question solved selecting answer using superficial information eg local context similarity global term frequency thus fail test essential integrative aspect RC encourage progress deeper comprehension language present new dataset set task reader must answer question story reading entire book movie script task designed successfully answering question requires understanding underlying narrative rather relying shallow pattern matching salience show although human solve task easily standard RC model struggle task presented provide analysis dataset challenge present Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Rajesh Bordawekar', 'Bortik Bandyopadhyay', 'Oded Shmueli']

1712.07199v1

We propose Cognitive Databases, an approach for transparently enabling Artificial Intelligence (AI) capabilities in relational databases. A novel aspect of our design is to first view the structured data source as meaningful unstructured text, and then use the text to build an unsupervised neural network model using a Natural Language Processing (NLP) technique called word embedding. This model captures the hidden inter-/intra-column relationships between database tokens of different types. For each database token, the model includes a vector that encodes contextual semantic relationships. We seamlessly integrate the word embedding model into existing SQL query infrastructure and use it to enable a new class of SQL-based analytics queries called cognitive intelligence (CI) queries. CI queries use the model vectors to enable complex queries such as semantic matching, inductive reasoning queries such as analogies, predictive queries using entities not present in a database, and, more generally, using knowledge from external sources. We demonstrate unique capabilities of Cognitive Databases using an Apache Spark based prototype to execute inductive reasoning CI queries over a multi-modal database containing text and images. We believe our first-of-a-kind system exemplifies using AI functionality to endow relational databases with capabilities that were previously very hard to realize in practice.

Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities

http://arxiv.org/pdf/1712.07199v1

Title Cognitive Database Step towards Endowing Relational Databases Artificial Intelligence Capabilities Summary propose Cognitive Databases approach transparently enabling Artificial Intelligence AI capability relational database novel aspect design first view structured data source meaningful unstructured text use text build unsupervised neural network model using Natural Language Processing NLP technique called word embedding model capture hidden interintracolumn relationship database token different type database token model includes vector encodes contextual semantic relationship seamlessly integrate word embedding model existing SQL query infrastructure use enable new class SQLbased analytics query called cognitive intelligence CI query CI query use model vector enable complex query semantic matching inductive reasoning query analogy predictive query using entity present database generally using knowledge external source demonstrate unique capability Cognitive Databases using Apache Spark based prototype execute inductive reasoning CI query multimodal database containing text image believe firstofakind system exemplifies using AI functionality endow relational database capability previously hard realize practice Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iñigo Casanueva', 'Paweł Budzianowski', 'Pei-Hao Su', 'Stefan Ultes', 'Lina Rojas-Barahona', 'Bo-Hsiang Tseng', 'Milica Gašić']

1803.03232v1

Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation. Traditional RL algorithms, however, fail to scale to large domains due to the curse of dimensionality. We propose a novel Dialogue Management architecture, based on Feudal RL, which decomposes the decision into two steps; a first step where a master policy selects a subset of primitive actions, and a second step where a primitive action is chosen from the selected subset. The structural information included in the domain ontology is used to abstract the dialogue state space, taking the decisions at each step using different parts of the abstracted state. This, combined with an information sharing mechanism between slots, increases the scalability to large domains. We show that an implementation of this approach, based on Deep-Q Networks, significantly outperforms previous state of the art in several dialogue domains and environments, without the need of any additional reward signal.

Feudal Reinforcement Learning for Dialogue Management in Large Domains

http://arxiv.org/pdf/1803.03232v1

Title Feudal Reinforcement Learning Dialogue Management Large Domains Summary Reinforcement learning RL promising approach solve dialogue policy optimisation Traditional RL algorithm however fail scale large domain due curse dimensionality propose novel Dialogue Management architecture based Feudal RL decomposes decision two step first step master policy selects subset primitive action second step primitive action chosen selected subset structural information included domain ontology used abstract dialogue state space taking decision step using different part abstracted state combined information sharing mechanism slot increase scalability large domain show implementation approach based DeepQ Networks significantly outperforms previous state art several dialogue domain environment without need additional reward signal Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Stephen Merity', 'Nitish Shirish Keskar', 'Richard Socher']

1803.08240v1

Many of the leading approaches in language modeling introduce novel, complex and specialized architectures. We take existing state-of-the-art word level language models based on LSTMs and QRNNs and extend them to both larger vocabularies as well as character-level granularity. When properly tuned, LSTMs and QRNNs achieve state-of-the-art results on character-level (Penn Treebank, enwik8) and word-level (WikiText-103) datasets, respectively. Results are obtained in only 12 hours (WikiText-103) to 2 days (enwik8) using a single modern GPU.

An Analysis of Neural Language Modeling at Multiple Scales

http://arxiv.org/pdf/1803.08240v1

Title Analysis Neural Language Modeling Multiple Scales Summary Many leading approach language modeling introduce novel complex specialized architecture take existing stateoftheart word level language model based LSTMs QRNNs extend larger vocabulary well characterlevel granularity properly tuned LSTMs QRNNs achieve stateoftheart result characterlevel Penn Treebank enwik8 wordlevel WikiText103 datasets respectively Results obtained 12 hour WikiText103 2 day enwik8 using single modern GPU Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andreas Schwarz', 'Christian Huemmer', 'Roland Maas', 'Walter Kellermann']

1410.2479v2

We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, and represents the relative amount of diffuse noise in each time and frequency bin. It is shown that using the diffuseness feature as an additional input to a DNN-based acoustic model leads to a reduced word error rate for the REVERB challenge corpus, both compared to logmelspec features extracted from noisy signals, and features enhanced by spectral subtraction.

Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments

http://arxiv.org/pdf/1410.2479v2

Title Spatial Diffuseness Features DNNBased Speech Recognition Noisy Reverberant Environments Summary propose spatial diffuseness feature deep neural network DNNbased automatic speech recognition improve recognition accuracy reverberant noisy environment feature computed realtime multiple microphone signal without requiring knowledge estimation direction arrival represents relative amount diffuse noise time frequency bin shown using diffuseness feature additional input DNNbased acoustic model lead reduced word error rate REVERB challenge corpus compared logmelspec feature extracted noisy signal feature enhanced spectral subtraction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yoon Kim', 'Yacine Jernite', 'David Sontag', 'Alexander M. Rush']

1508.06615v4

We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-of-the-art despite having 60% fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian), the model outperforms word-level/morpheme-level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.

Character-Aware Neural Language Models

http://arxiv.org/pdf/1508.06615v4

Title CharacterAware Neural Language Models Summary describe simple neural language model relies characterlevel input Predictions still made wordlevel model employ convolutional neural network CNN highway network character whose output given long shortterm memory LSTM recurrent neural network language model RNNLM English Penn Treebank model par existing stateoftheart despite 60 fewer parameter language rich morphology Arabic Czech French German Spanish Russian model outperforms wordlevelmorphemelevel LSTM baseline fewer parameter result suggest many language character input sufficient language modeling Analysis word representation obtained character composition part model reveals model able encode character semantic orthographic information Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Krzysztof Wołk', 'Krzysztof Marasek']

1509.08644v1

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. The main machine translation evaluation metrics have also been used in analysis of the systems. A comparison and implementation of a real-time medical translator is the main focus of our experiments.

Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts

http://arxiv.org/pdf/1509.08644v1

Title Neuralbased machine translation medical text domain Based European Medicines Agency leaflet text Summary quality machine translation rapidly evolving Today one find several machine translation system web provide reasonable translation although system perfect specific domain quality may decrease recently proposed approach domain neural machine translation aim building jointlytuned single neural network maximizes translation performance different approach traditional statistical machine translation Recently proposed neural machine translation model often belong encoderdecoder family source sentence encoded fixed length vector turn decoded generate translation present research examines effect different training method PolishEnglish Machine Translation system used medical data European Medicines Agency parallel text corpus used basis training neural statistical networkbased translation system main machine translation evaluation metric also used analysis system comparison implementation realtime medical translator main focus experiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tsung-Hsien Wen', 'Milica Gasic', 'Nikola Mrksic', 'Lina M. Rojas-Barahona', 'Pei-Hao Su', 'Stefan Ultes', 'David Vandyke', 'Steve Young']

1606.03352v1

Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector. The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two. Secondly, the discriminative power and transparency of the conditioning vector is key to providing both model interpretability and better performance. Thirdly, snapshot learning leads to consistent performance improvements independent of which architecture is used.

Conditional Generation and Snapshot Learning in Neural Dialogue Systems

http://arxiv.org/pdf/1606.03352v1

Title Conditional Generation Snapshot Learning Neural Dialogue Systems Summary Recently variety LSTMbased conditional language model LM applied across range language generation task work study various model architecture different way represent aggregate source information endtoend neural dialogue system framework method called snapshot learning also proposed facilitate learning supervised sequential signal applying companion crossentropy objective function conditioning vector experimental analytical result demonstrate firstly competition occurs conditioning vector LM differing architecture provide different tradeoff two Secondly discriminative power transparency conditioning vector key providing model interpretability better performance Thirdly snapshot learning lead consistent performance improvement independent architecture used Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Julien Perez', 'Fei Liu']

1606.04052v5

In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules. This paper introduces a novel method of dialog state tracking based on the general paradigm of machine reading and proposes to solve it using an End-to-End Memory Network, MemN2N, a memory-enhanced neural network architecture. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset. The corpus has been converted for the occasion in order to frame the hidden state variable inference as a question-answering task based on a sequence of utterances extracted from a dialog. We show that the proposed tracker gives encouraging results. Then, we propose to extend the DSTC-2 dataset with specific reasoning capabilities requirement like counting, list maintenance, yes-no question answering and indefinite knowledge management. Finally, we present encouraging results using our proposed MemN2N based tracking model.

Dialog state tracking, a machine reading approach using Memory Network

http://arxiv.org/pdf/1606.04052v5

Title Dialog state tracking machine reading approach using Memory Network Summary endtoend dialog system aim dialog state tracking accurately estimate compact representation current dialog status sequence noisy observation produced speech recognition natural language understanding module paper introduces novel method dialog state tracking based general paradigm machine reading proposes solve using EndtoEnd Memory Network MemN2N memoryenhanced neural network architecture evaluate proposed approach second Dialog State Tracking Challenge DSTC2 dataset corpus converted occasion order frame hidden state variable inference questionanswering task based sequence utterance extracted dialog show proposed tracker give encouraging result propose extend DSTC2 dataset specific reasoning capability requirement like counting list maintenance yesno question answering indefinite knowledge management Finally present encouraging result using proposed MemN2N based tracking model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sándor Darányi', 'Peter Wittek', 'Konstantinos Konstantinidis', 'Symeon Papadopoulos', 'Efstratios Kontopoulos']

1608.01298v1

In accessibility tests for digital preservation, over time we experience drifts of localized and labelled content in statistical models of evolving semantics represented as a vector field. This articulates the need to detect, measure, interpret and model outcomes of knowledge dynamics. To this end we employ a high-performance machine learning algorithm for the training of extremely large emergent self-organizing maps for exploratory data analysis. The working hypothesis we present here is that the dynamics of semantic drifts can be modeled on a relaxed version of Newtonian mechanics called social mechanics. By using term distances as a measure of semantic relatedness vs. their PageRank values indicating social importance and applied as variable `term mass', gravitation as a metaphor to express changes in the semantic content of a vector field lends a new perspective for experimentation. From `term gravitation' over time, one can compute its generating potential whose fluctuations manifest modifications in pairwise term similarity vs. social importance, thereby updating Osgood's semantic differential. The dataset examined is the public catalog metadata of Tate Galleries, London.

A Physical Metaphor to Study Semantic Drift

http://arxiv.org/pdf/1608.01298v1

Title Physical Metaphor Study Semantic Drift Summary accessibility test digital preservation time experience drift localized labelled content statistical model evolving semantics represented vector field articulates need detect measure interpret model outcome knowledge dynamic end employ highperformance machine learning algorithm training extremely large emergent selforganizing map exploratory data analysis working hypothesis present dynamic semantic drift modeled relaxed version Newtonian mechanic called social mechanic using term distance measure semantic relatedness v PageRank value indicating social importance applied variable term mass gravitation metaphor express change semantic content vector field lends new perspective experimentation term gravitation time one compute generating potential whose fluctuation manifest modification pairwise term similarity v social importance thereby updating Osgoods semantic differential dataset examined public catalog metadata Tate Galleries London Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Franck Dernoncourt', 'Ji Young Lee']

1609.08703v1

Systems based on artificial neural networks (ANNs) have achieved state-of-the-art results in many natural language processing tasks. Although ANNs do not require manually engineered features, ANNs have many hyperparameters to be optimized. The choice of hyperparameters significantly impacts models' performances. However, the ANN hyperparameters are typically chosen by manual, grid, or random search, which either requires expert experiences or is computationally expensive. Recent approaches based on Bayesian optimization using Gaussian processes (GPs) is a more systematic way to automatically pinpoint optimal or near-optimal machine learning hyperparameters. Using a previously published ANN model yielding state-of-the-art results for dialog act classification, we demonstrate that optimizing hyperparameters using GP further improves the results, and reduces the computational time by a factor of 4 compared to a random search. Therefore it is a useful technique for tuning ANN models to yield the best performances for natural language processing tasks.

Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification

http://arxiv.org/pdf/1609.08703v1

Title Optimizing Neural Network Hyperparameters Gaussian Processes Dialog Act Classification Summary Systems based artificial neural network ANNs achieved stateoftheart result many natural language processing task Although ANNs require manually engineered feature ANNs many hyperparameters optimized choice hyperparameters significantly impact model performance However ANN hyperparameters typically chosen manual grid random search either requires expert experience computationally expensive Recent approach based Bayesian optimization using Gaussian process GPs systematic way automatically pinpoint optimal nearoptimal machine learning hyperparameters Using previously published ANN model yielding stateoftheart result dialog act classification demonstrate optimizing hyperparameters using GP improves result reduces computational time factor 4 compared random search Therefore useful technique tuning ANN model yield best performance natural language processing task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hans Krupakar', 'Keerthika Rajvel', 'Bharathi B', 'Angel Deborah S', 'Vallidevi Krishnamurthy']

1610.03934v1

Speech Translation has always been about giving source text or audio input and waiting for system to give translated output in desired form. In this paper, we present the Acoustic Dialect Decoder (ADD) - a voice to voice ear-piece translation device. We introduce and survey the recent advances made in the field of Speech Engineering, to employ in the ADD, particularly focusing on the three major processing steps of Recognition, Translation and Synthesis. We tackle the problem of machine understanding of natural language by designing a recognition unit for source audio to text, a translation unit for source language text to target language text, and a synthesis unit for target language text to target language speech. Speech from the surroundings will be recorded by the recognition unit present on the ear-piece and translation will start as soon as one sentence is successfully read. This way, we hope to give translated output as and when input is being read. The recognition unit will use Hidden Markov Models (HMMs) Based Tool-Kit (HTK), hybrid RNN systems with gated memory cells, and the synthesis unit, HMM based speech synthesis system HTS. This system will initially be built as an English to Tamil translation device.

A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder

http://arxiv.org/pdf/1610.03934v1

Title Survey Voice Translation Methodologies Acoustic Dialect Decoder Summary Speech Translation always giving source text audio input waiting system give translated output desired form paper present Acoustic Dialect Decoder ADD voice voice earpiece translation device introduce survey recent advance made field Speech Engineering employ ADD particularly focusing three major processing step Recognition Translation Synthesis tackle problem machine understanding natural language designing recognition unit source audio text translation unit source language text target language text synthesis unit target language text target language speech Speech surroundings recorded recognition unit present earpiece translation start soon one sentence successfully read way hope give translated output input read recognition unit use Hidden Markov Models HMMs Based ToolKit HTK hybrid RNN system gated memory cell synthesis unit HMM based speech synthesis system HTS system initially built English Tamil translation device Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mark Neumann', 'Pontus Stenetorp', 'Sebastian Riedel']

1610.07647v2

Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading. In this work, we demonstrate the effectiveness of adaptive computation for learning the number of inference steps required for examples of different complexity and that learning the correct number of inference steps is difficult. We introduce the first model involving Adaptive Computation Time which provides a small performance benefit on top of a similar model without an adaptive component as well as enabling considerable insight into the reasoning process of the model.

Learning to Reason With Adaptive Computation

http://arxiv.org/pdf/1610.07647v2

Title Learning Reason Adaptive Computation Summary Multihop inference necessary machine learning system successfully solve task Recognising Textual Entailment Machine Reading work demonstrate effectiveness adaptive computation learning number inference step required example different complexity learning correct number inference step difficult introduce first model involving Adaptive Computation Time provides small performance benefit top similar model without adaptive component well enabling considerable insight reasoning process model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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