pwc-archive/papers-with-abstracts huggingface dataset:
Interactive Learning of Environment Dynamics for Sequential Tasks
Authors: Robert Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts
Date: 2019-07-19 00:00:00

In order for robots and other artificial agents to efficiently learn to perform useful tasks defined by an end user, they must understand not only the goals of those tasks, but also the structure and dynamics of that user's environment. While existing work has looked at how the goals of a task can be inferred from a human teacher, the agent is often left to learn about the environment on its own. To address this limitation, we develop an algorithm, Behavior Aware Modeling (BAM), which incorporates a teacher's knowledge into a model of the transition dynamics of an agent's environment. We evaluate BAM both in simulation and with real human teachers, learning from a combination of task demonstrations and evaluative feedback, and show that it can outperform approaches which do not explicitly consider this source of dynamics knowledge.
Exploring sentence informativeness
Authors: Syrielle Montariol, Aina Garí Soler, Alexandre Allauzen
Date: 2019-07-19 00:00:00
Proceeding: JEPTALNRECITAL 2019 7
Tasks: Informativeness, Sentence, Word Embeddings

This study is a preliminary exploration of the concept of informativeness -how much information a sentence gives about a word it contains- and its potential benefits to building quality word representations from scarce data. We propose several sentence-level classifiers to predict informativeness, and we perform a manual annotation on a set of sentences. We conclude that these two measures correspond to different notions of informativeness. However, our experiments show that using the classifiers' predictions to train word embeddings has an impact on embedding quality.
Enhancing magic sets with an application to ontological reasoning
Authors: Mario Alviano, Nicola Leone, Pierfrancesco Veltri, Jessica Zangari
Date: 2019-07-19 00:00:00
Tasks: Negation

Magic sets are a Datalog to Datalog rewriting technique to optimize query answering. The rewritten program focuses on a portion of the stable model(s) of the input program which is sufficient to answer the given query. However, the rewriting may introduce new recursive definitions, which can involve even negation and aggregations, and may slow down program evaluation. This paper enhances the magic set technique by preventing the creation of (new) recursive definitions in the rewritten program. It turns out that the new version of magic sets is closed for Datalog programs with stratified negation and aggregations, which is very convenient to obtain efficient computation of the stable model of the rewritten program. Moreover, the rewritten program is further optimized by the elimination of subsumed rules and by the efficient handling of the cases where binding propagation is lost. The research was stimulated by a challenge on the exploitation of Datalog/\textsc{dlv} for efficient reasoning on large ontologies. All proposed techniques have been hence implemented in the \textsc{dlv} system, and tested for ontological reasoning, confirming their effectiveness. Under consideration for publication in Theory and Practice of Logic Programming.
Representation Learning for Classical Planning from Partially Observed Traces
Authors: Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Jinxia Lin, Yanan Liu
Date: 2019-07-19 00:00:00
Tasks: Graph Neural Network, Representation Learning

Specifying a complete domain model is time-consuming, which has been a bottleneck of AI planning technique application in many real-world scenarios. Most classical domain-model learning approaches output a domain model in the form of the declarative planning language, such as STRIPS or PDDL, and solve new planning instances by invoking an existing planner. However, planning in such a representation is sensitive to the accuracy of the learned domain model which probably cannot be used to solve real planning problems. In this paper, to represent domain models in a vectorization representation way, we propose a novel framework based on graph neural network (GNN) integrating model-free learning and model-based planning, called LP-GNN. By embedding propositions and actions in a graph, the latent relationship between them is explored to form a domain-specific heuristics. We evaluate our approach on five classical planning domains, comparing with the classical domain-model learner ARMS. The experimental results show that the domain models learned by our approach are much more effective on solving real planning problems.
Greedy Optimized Multileaving for Personalization
Authors: Kojiro Iizuka, Takeshi Yoneda, Yoshifumi Seki
Date: 2019-07-19 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval

Personalization plays an important role in many services. To evaluate personalized rankings, online evaluation, such as A/B testing, is widely used today. Recently, multileaving has been found to be an efficient method for evaluating rankings in information retrieval fields. This paper describes the first attempt to optimize the multileaving method for personalization settings. We clarify the challenges of applying this method to personalized rankings. Then, to solve these challenges, we propose greedy optimized multileaving (GOM) with a new credit feedback function. The empirical results showed that GOM was stable for increasing ranking lengths and the number of rankers. We implemented GOM on our actual news recommender systems, and compared its online performance. The results showed that GOM evaluated the personalized rankings precisely, with significantly smaller sample sizes (< 1/10) than A/B testing.
I Stand With You: Using Emojis to Study Solidarity in Crisis Events
Authors: Sashank Santhanam, Vidhushini Srinivasan, Shaina Glass, Samira Shaikh
Date: 2019-07-19 00:00:00

We study how emojis are used to express solidarity in social media in the context of two major crisis events - a natural disaster, Hurricane Irma in 2017 and terrorist attacks that occurred on November 2015 in Paris. Using annotated corpora, we first train a recurrent neural network model to classify expressions of solidarity in text. Next, we use these expressions of solidarity to characterize human behavior in online social networks, through the temporal and geospatial diffusion of emojis. Our analysis reveals that emojis are a powerful indicator of sociolinguistic behaviors (solidarity) that are exhibited on social media as the crisis events unfold.
An AI-based, Multi-stage detection system of banking botnets
Authors: Li Ling, Zhiqiang Gao, Michael A Silas, Ian Lee, Erwan A Le Doeuff
Date: 2019-07-18 00:00:00

Banking Trojans, botnets are primary drivers of financially-motivated cybercrime. In this paper, we first analyzed how an APT-based banking botnet works step by step through the whole lifecycle. Specifically, we present a multi-stage system that detects malicious banking botnet activities which potentially target the organizations. The system leverages Cyber Data Lake as well as multiple artificial intelligence techniques at different stages. The evaluation results using public datasets showed that Deep Learning based detections were highly successful compared with baseline models.
Joint Learning of Named Entity Recognition and Entity Linking
Authors: Pedro Henrique Martins, Zita Marinho, André F. T. Martins
Date: 2019-07-18 00:00:00
Proceeding: ACL 2019 7
Tasks: Entity Linking, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. However, most entity linking approaches disregard the mention detection part, assuming that the correct mentions have been previously detected. In this paper, we perform joint learning of NER and EL to leverage their relatedness and obtain a more robust and generalisable system. For that, we introduce a model inspired by the Stack-LSTM approach (Dyer et al., 2015). We observe that, in fact, doing multi-task learning of NER and EL improves the performance in both tasks when comparing with models trained with individual objectives. Furthermore, we achieve results competitive with the state-of-the-art in both NER and EL.
SLATE: A Super-Lightweight Annotation Tool for Experts
Authors: Jonathan K. Kummerfeld
Date: 2019-07-18 00:00:00
Proceeding: ACL 2019 7
Tasks: Management

Many annotation tools have been developed, covering a wide variety of tasks and providing features like user management, pre-processing, and automatic labeling. However, all of these tools use Graphical User Interfaces, and often require substantial effort to install and configure. This paper presents a new annotation tool that is designed to fill the niche of a lightweight interface for users with a terminal-based workflow. Slate supports annotation at different scales (spans of characters, tokens, and lines, or a document) and of different types (free text, labels, and links), with easily customisable keybindings, and unicode support. In a user study comparing with other tools it was consistently the easiest to install and use. Slate fills a need not met by existing systems, and has already been used to annotate two corpora, one of which involved over 250 hours of annotation effort.
Neural Probabilistic Logic Programming in DeepProbLog
Authors: Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt
Date: 2019-07-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Learning, Program induction

We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.
Investigating Target Set Reduction for End-to-End Speech Recognition of Hindi-English Code-Switching Data
Authors: Kunal Dhawan, Ganji Sreeram, Kumar Priyadarshi, Rohit Sinha
Date: 2019-07-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

End-to-end (E2E) systems are fast replacing the conventional systems in the domain of automatic speech recognition. As the target labels are learned directly from speech data, the E2E systems need a bigger corpus for effective training. In the context of code-switching task, the E2E systems face two challenges: (i) the expansion of the target set due to multiple languages involved, and (ii) the lack of availability of sufficiently large domain-specific corpus. Towards addressing those challenges, we propose an approach for reducing the number of target labels for reliable training of the E2E systems on limited data. The efficacy of the proposed approach has been demonstrated on two prominent architectures, namely CTC-based and attention-based E2E networks. The experimental validations are performed on a recently created Hindi-English code-switching corpus. For contrast purpose, the results for the full target set based E2E system and a hybrid DNN-HMM system are also reported.
Modified swarm-based metaheuristics enhance Gradient Descent initialization performance: Application for EEG spatial filtering
Authors: Mojtaba Moattari, Mohammad Hassan Moradi, Reza Boostani
Date: 2019-06-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Gradient Descent (GD) approximators often fail in the solution space with multiple scales of convexities, i.e., in subspace learning and neural network scenarios. To handle that, one solution is to run GD multiple times from different randomized initial states and select the best solution over all experiments. However, this idea is proved impractical in plenty of cases. Even Swarm-based optimizers like Particle Swarm Optimization (PSO) or Imperialistic Competitive Algorithm (ICA), as commonly used GD initializers, have failed to find optimal solutions in some applications. In this paper, Swarm-based optimizers like ICA and PSO are modified by a new optimization framework to improve GD optimization performance. This improvement is for applications with high number of convex localities in multiple scales. Performance of the proposed method is analyzed in a nonlinear subspace filtering objective function over EEG data. The proposed metaheuristic outperforms commonly used baseline optimizers as GD initializers in both the EEG classification accuracy and EEG loss function fitness. The optimizers have been also compared to each other in some of CEC 2014 benchmark functions, where again our method outperforms other algorithms.
Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track
Authors: M. Jorge Cardoso, Aasa Feragen, Ben Glocker, Ender Konukoglu, Ipek Oguz, Gozde Unal, Tom Vercauteren
Date: 2019-05-21 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019. Note that only accepted extended abstracts are listed here, the Proceedings of the MIDL 2019 Full Paper Track are published as Volume 102 of the Proceedings of Machine Learning Research (PMLR) http://proceedings.mlr.press/v102/.
Single-View Reconstruction via Joint Analysis of Image and Shape Collections
Authors: Qixing Huang, Hai Wang, Vladlen Koltun
Date: 2015-12-01 00:00:00
Proceeding: ACM Transactions on Graphics 2015 12
Tasks: 3D Reconstruction

We present an approach to automatic 3D reconstruction of objects depicted in Web images. The approach reconstructs objects from single views. The key idea is to jointly analyze a collection of images of different objects along with a smaller collection of existing 3D models. The images are analyzed and reconstructed together. Joint analysis regularizes the formulated optimization problems, stabilizes correspondence estimation, and leads to reasonable reproduction of object appearance without traditional multi-view cues.
mlpack 3: a fast, flexible machine learning library
Authors: Ryan R. Curtin, Marcus Edel, Mikhail Lozhnikov, Yannis Mentekidis, Sumedh Ghaisas, Shangtong Zhang
Date: 2018-06-18 00:00:00
Proceeding: Journal of Open Source Software 2018 6
Tasks: Benchmarking, BIG-bench Machine Learning, Clustering, GPU, Reinforcement Learning

In the past several years, the field of machine learning has seen an explosion of interest and excitement, with hundreds or thousands of algorithms developed for different tasks every year. But a primary problem faced by the field is the ability to scale to larger and larger data -- since it is known that training on larger datasets typically produces better results. Therefore, the development of new algorithms for the continued growth of the field depends largely on the existence of good tooling and libraries that enable researchers and practitioners to quickly prototype and develop solutions. Simultaneously, useful libraries must also be efficient and well-implemented. This has motivated our development of mlpack. mlpack is a flexible and fast machine learning library written in C++ that has bindings that allow use from the command-line and from Python, with support for other languages in active development. mlpack has been developed actively for over 10 years, with over 100 contributors from around the world, and is a frequent mentoring organization in the Google Summer of Code program. If used in C++, the library allows flexibility with no speed penalty through policy-based design and template metaprogramming; but bindings are available to other languages, which allow easy use of the fast mlpack codebase. For fast linear algebra, mlpack is built on the Armadillo C++ matrix library, which in turn can use an optimized BLAS implementation such as OpenBLAS or even NVBLAS which would allow mlpack algorithms to be run on the GPU. In order to provide fast code, template metaprogramming is used throughout the library to reduce runtime overhead by performing any possible computations and optimizations at compile time. An automatic benchmarking system is developed and used to test the efficiency of mlpack's algorithms. mlpack contains a number of standard machine learning algorithms, such as logistic regression, random forests, and k-means clustering, and also contains cutting-edge techniques such as a compile-time optimized deep learning and reinforcement learning framework, dual-tree algorithms for nearest neighbor search and other tasks, a generic optimization framework with numerous optimizers, a generic hyper-parameter tuner, and other recently published machine learning algorithms. For a more comprehensive introduction to mlpack, see the website at http://www.mlpack.org/
Visual Vibrometry: Estimating MaterialProperties from Small Motions in Video
Authors: Abe Davis, Katherine L. Bouman, Justin G. Chen, Michael Rubinstein, Oral Buyukozturk, Fredo Durand, William T. Freeman
Date: 2017-04-15 00:00:00
Proceeding: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,  VOL. 39, NO. 4 2017 4
Tasks: Object, Scene Understanding

The estimation of material properties is important for scene understanding, with many applications in vision, robotics, andstructural engineering. This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infermaterial properties from small, often imperceptible motions in video. Objects tend to vibrate in a set of preferred modes. Thefrequencies of these modes depend on the structure and material properties of an object. We show that by extracting these frequenciesfrom video of a vibrating object, we can often make inferences about that object’s material properties. We demonstrate our approach byestimating material properties for a variety of objects by observing their motion in high-speed and regular frame rate video.
Visual Vibrometry: Estimating Material Properties from Small Motions in Video
Authors: Abe Davis, Katherine L. Bouman, Justin G. Chen, Michael Rubinstein, Oral Buyukozturk, Fredo Durand, William T. Freeman
Date: 2017-04-15 00:00:00
Proceeding: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,  VOL. 39, NO. 4 2017 4
Tasks: Scene Understanding

The  estimation  of  material  properties  is  important  for scene  understanding,  with  many  applications  in  vision,  robotics, and structural engineering.  This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motion in video. Objects tend to vibrate in a set of preferred modes.  The shapes and frequencies ofthese modes depend on the structure and material properties of an object.  Focusing on the case where geometry is known or fixed, we show how information about an object’s modes of vibration can be extracted from video and used to make inferences about that object’s material properties. We demonstrate our approach by estimating material properties for a variety of rods and fabrics by passively observing their motion in high-speed and regular frame rate video
Automatic detection of rare pathologies in fundus photographs using few-shot learning
Authors: Gwenolé Quellec, Mathieu Lamard, Pierre-Henri Conze, Pascale Massin, Béatrice Cochener
Date: 2019-07-22 00:00:00
Tasks: Few-Shot Learning, One-Shot Learning, Transfer Learning

In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, these datasets were used to train automatic detectors for DR and a few other frequent pathologies, with the goal to automate screening. One challenge limits the adoption of such systems so far: automatic detectors ignore rare conditions that ophthalmologists currently detect, such as papilledema or anterior ischemic optic neuropathy. The reason is that standard deep learning requires too many examples of these conditions. However, this limitation can be addressed with few-shot learning, a machine learning paradigm where a classifier has to generalize to a new category not seen in training, given only a few examples of this category. This paper presents a new few-shot learning framework that extends convolutional neural networks (CNNs), trained for frequent conditions, with an unsupervised probabilistic model for rare condition detection. It is based on the observation that CNNs often perceive photographs containing the same anomalies as similar, even though these CNNs were trained to detect unrelated conditions. This observation was based on the t-SNE visualization tool, which we decided to incorporate in our probabilistic model. Experiments on a dataset of 164,660 screening examinations from the OPHDIAT screening network show that 37 conditions, out of 41, can be detected with an area under the ROC curve (AUC) greater than 0.8 (average AUC: 0.938). In particular, this framework significantly outperforms other frameworks for detecting rare conditions, including multitask learning, transfer learning and Siamese networks, another few-shot learning solution. We expect these richer predictions to trigger the adoption of automated eye pathology screening, which will revolutionize clinical practice in ophthalmology.
Model-Driven Deep Learning for MIMO Detection
Authors: Hengtao He, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li
Date: 2019-07-22 00:00:00
Tasks: Deep Learning, model

In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft-input soft-output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection (JCESD), where the detector takes channel estimation error and channel statistics into consideration while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based MIMO detectors and exhibits superior robustness to various mismatches.
Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks
Authors: Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan, Jing-Jhih Lin
Date: 2019-07-22 00:00:00
Tasks: Lane Detection, Segmentation, Semantic Segmentation

Lane detection plays an important role in a self-driving vehicle. Several studies leverage a semantic segmentation network to extract robust lane features, but few of them can distinguish different types of lanes. In this paper, we focus on the problem of multi-class lane semantic segmentation. Based on the observation that the lane is a small-size and narrow-width object in a road scene image, we propose two techniques, Feature Size Selection (FSS) and Degressive Dilation Block (DD Block). The FSS allows a network to extract thin lane features using appropriate feature sizes. To acquire fine-grained spatial information, the DD Block is made of a series of dilated convolutions with degressive dilation rates. Experimental results show that the proposed techniques provide obvious improvement in accuracy, while they achieve the same or faster inference speed compared to the baseline system, and can run at real-time on high-resolution images.