pwc-archive/papers-with-abstracts huggingface dataset:
FasterSeg: Searching for Faster Real-time Semantic Segmentation
Authors: Wuyang Chen, Xinyu Gong, Xian-Ming Liu, Qian Zhang, Yuan Li, Zhangyang Wang
Date: 2019-12-23 00:00:00
Proceeding: ICLR 2020 1
Tasks: Neural Architecture Search, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neural architecture search (NAS), FasterSeg is discovered from a novel and broader search space integrating multi-resolution branches, that has been recently found to be vital in manually designed segmentation models. To better calibrate the balance between the goals of high accuracy and low latency, we propose a decoupled and fine-grained latency regularization, that effectively overcomes our observed phenomenons that the searched networks are prone to "collapsing" to low-latency yet poor-accuracy models. Moreover, we seamlessly extend FasterSeg to a new collaborative search (co-searching) framework, simultaneously searching for a teacher and a student network in the same single run. The teacher-student distillation further boosts the student model's accuracy. Experiments on popular segmentation benchmarks demonstrate the competency of FasterSeg. For example, FasterSeg can run over 30% faster than the closest manually designed competitor on Cityscapes, while maintaining comparable accuracy.
PSNet: Parametric Sigmoid Norm Based CNN for Face Recognition
Authors: Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey
Date: 2019-12-05 00:00:00
Tasks: Face Recognition

The Convolutional Neural Networks (CNN) have become very popular recently due to its outstanding performance in various computer vision applications. It is also used over widely studied face recognition problem. However, the existing layers of CNN are unable to cope with the problem of hard examples which generally produce lower class scores. Thus, the existing methods become biased towards the easy examples. In this paper, we resolve this problem by incorporating a Parametric Sigmoid Norm (PSN) layer just before the final fully-connected layer. We propose a PSNet CNN model by using the PSN layer. The PSN layer facilitates high gradient flow for harder examples as compared to easy examples. Thus, it forces the network to learn the visual characteristics of hard examples. We conduct the face recognition experiments to test the performance of PSN layer. The suitability of the PSN layer with different loss functions is also experimented. The widely used Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) datasets are used in the experiments. The experimental results confirm the relevance of the proposed PSN layer.
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
Authors: Xin Chen, Lingxi Xie, Jun Wu, Qi Tian
Date: 2019-12-23 00:00:00
Tasks: GPU, Neural Architecture Search

With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks. However, it remains unclear if the searched architecture can transfer across different types of tasks as manually designed ones did. This paper puts forward this problem, referred to as NAS in the wild, which explores the possibility of finding the optimal architecture in a proxy dataset and then deploying it to mostly unseen scenarios. We instantiate this setting using a currently popular algorithm named differentiable architecture search (DARTS), which often suffers unsatisfying performance while being transferred across different tasks. We argue that the accuracy drop originates from the formulation that uses a super-network for search but a sub-network for re-training. The different properties of these stages have resulted in a significant optimization gap, and consequently, the architectural parameters "over-fit" the super-network. To alleviate the gap, we present a progressive method that gradually increases the network depth during the search stage, which leads to the Progressive DARTS (P-DARTS) algorithm. With a reduced search cost (7 hours on a single GPU), P-DARTS achieves improved performance on both the proxy dataset (CIFAR10) and a few target problems (ImageNet classification, COCO detection and three ReID benchmarks). Our code is available at \url{https://github.com/chenxin061/pdarts}.
A physics-aware machine to predict extreme events in turbulence
Authors: Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
Date: 2019-12-23 00:00:00

We propose a physics-aware machine learning method to time-accurately predict extreme events in a turbulent flow. The method combines two radically different approaches: empirical modelling based on reservoir computing, which learns the chaotic dynamics from data only, and physical modelling based on conservation laws. We show that the combination of the two approaches is able to predict the occurrence and amplitude of extreme events in the self-sustaining process in turbulence-the abrupt transitions from turbulent to quasi-laminar states-which cannot be achieved by using either approach separately. This opens up new possibilities for enhancing synergistically data-driven methods with physical knowledge for the accurate prediction of extreme events in chaotic dynamical systems.
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
Authors: William Lotter, Abdul Rahman Diab, Bryan Haslam, Jiye G. Kim, Giorgia Grisot, Eric Wu, Kevin Wu, Jorge Onieva Onieva, Jerrold L. Boxerman, Meiyun Wang, Mack Bandler, Gopal Vijayaraghavan, A. Gregory Sorensen
Date: 2019-12-23 00:00:00
Tasks: Breast Cancer Detection, Deep Learning

Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by health organizations worldwide and has been estimated to decrease breast cancer mortality by 20-40%. Nevertheless, significant false positive and false negative rates, as well as high interpretation costs, leave opportunities for improving quality and access. To address these limitations, there has been much recent interest in applying deep learning to mammography; however, obtaining large amounts of annotated data poses a challenge for training deep learning models for this purpose, as does ensuring generalization beyond the populations represented in the training dataset. Here, we present an annotation-efficient deep learning approach that 1) achieves state-of-the-art performance in mammogram classification, 2) successfully extends to digital breast tomosynthesis (DBT; "3D mammography"), 3) detects cancers in clinically-negative prior mammograms of cancer patients, 4) generalizes well to a population with low screening rates, and 5) outperforms five-out-of-five full-time breast imaging specialists by improving absolute sensitivity by an average of 14%. Our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
Authors: Panagiotis Tsilifis, Iason Papaioannou, Daniel Straub, Fabio Nobile
Date: 2019-12-23 00:00:00
Tasks: Compressive Sensing, Computational Efficiency, Variational Inference

The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations. These challenges can be addressed by enforcing sparsity in the series representation through retaining only the most important basis terms. In this work, we present a novel sparse Bayesian learning technique for obtaining sparse Polynomial Chaos expansions which is based on a Relevance Vector Machine model and is trained using Variational Inference. The methodology shows great potential in high-dimensional data-driven settings using relatively few data points and achieves user-controlled sparse levels that are comparable to other methods such as compressive sensing. The proposed approach is illustrated on two numerical examples, a synthetic response function that is explored for validation purposes and a low-carbon steel plate with random Young's modulus and random loading, which is modeled by stochastic finite element with 38 input random variables.
Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning
Authors: Richard Li, Allan Jabri, Trevor Darrell, Pulkit Agrawal
Date: 2019-12-23 00:00:00
Tasks: Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Zero-shot Generalization

Learning robotic manipulation tasks using reinforcement learning with sparse rewards is currently impractical due to the outrageous data requirements. Many practical tasks require manipulation of multiple objects, and the complexity of such tasks increases with the number of objects. Learning from a curriculum of increasingly complex tasks appears to be a natural solution, but unfortunately, does not work for many scenarios. We hypothesize that the inability of the state-of-the-art algorithms to effectively utilize a task curriculum stems from the absence of inductive biases for transferring knowledge from simpler to complex tasks. We show that graph-based relational architectures overcome this limitation and enable learning of complex tasks when provided with a simple curriculum of tasks with increasing numbers of objects. We demonstrate the utility of our framework on a simulated block stacking task. Starting from scratch, our agent learns to stack six blocks into a tower. Despite using step-wise sparse rewards, our method is orders of magnitude more data-efficient and outperforms the existing state-of-the-art method that utilizes human demonstrations. Furthermore, the learned policy exhibits zero-shot generalization, successfully stacking blocks into taller towers and previously unseen configurations such as pyramids, without any further training.
Learning Singing From Speech
Authors: Liqiang Zhang, Chengzhu Yu, Heng Lu, Chao Weng, Yusong Wu, Xiang Xie, Zijin Li, Dong Yu
Date: 2019-12-20 00:00:00
Tasks: Speech Synthesis, Voice Conversion

We propose an algorithm that is capable of synthesizing high quality target speaker's singing voice given only their normal speech samples. The proposed algorithm first integrate speech and singing synthesis into a unified framework, and learns universal speaker embeddings that are shareable between speech and singing synthesis tasks. Specifically, the speaker embeddings learned from normal speech via the speech synthesis objective are shared with those learned from singing samples via the singing synthesis objective in the unified training framework. This makes the learned speaker embedding a transferable representation for both speaking and singing. We evaluate the proposed algorithm on singing voice conversion task where the content of original singing is covered with the timbre of another speaker's voice learned purely from their normal speech samples. Our experiments indicate that the proposed algorithm generates high-quality singing voices that sound highly similar to target speaker's voice given only his or her normal speech samples. We believe that proposed algorithm will open up new opportunities for singing synthesis and conversion for broader users and applications.
Modeling Intent, Dialog Policies and Response Adaptation for Goal-Oriented Interactions
Authors: Saurav Sahay, Shachi H. Kumar, Eda Okur, Haroon Syed, Lama Nachman
Date: 2019-12-20 00:00:00
Tasks: Intent Recognition

Building a machine learning driven spoken dialog system for goal-oriented interactions involves careful design of intents and data collection along with development of intent recognition models and dialog policy learning algorithms. The models should be robust enough to handle various user distractions during the interaction flow and should steer the user back into an engaging interaction for successful completion of the interaction. In this work, we have designed a goal-oriented interaction system where children can engage with agents for a series of interactions involving `Meet \& Greet' and `Simon Says' game play. We have explored various feature extractors and models for improved intent recognition and looked at leveraging previous user and system interactions in novel ways with attention models. We have also looked at dialog adaptation methods for entrained response selection. Our bootstrapped models from limited training data perform better than many baseline approaches we have looked at for intent recognition and dialog action prediction.
Exploring Context, Attention and Audio Features for Audio Visual Scene-Aware Dialog
Authors: Shachi H. Kumar, Eda Okur, Saurav Sahay, Jonathan Huang, Lama Nachman
Date: 2019-12-20 00:00:00
Tasks: Audio Classification, Visual Grounding

We are witnessing a confluence of vision, speech and dialog system technologies that are enabling the IVAs to learn audio-visual groundings of utterances and have conversations with users about the objects, activities and events surrounding them. Recent progress in visual grounding techniques and Audio Understanding are enabling machines to understand shared semantic concepts and listen to the various sensory events in the environment. With audio and visual grounding methods, end-to-end multimodal SDS are trained to meaningfully communicate with us in natural language about the real dynamic audio-visual sensory world around us. In this work, we explore the role of `topics' as the context of the conversation along with multimodal attention into such an end-to-end audio-visual scene-aware dialog system architecture. We also incorporate an end-to-end audio classification ConvNet, AclNet, into our models. We develop and test our approaches on the Audio Visual Scene-Aware Dialog (AVSD) dataset released as a part of the DSTC7. We present the analysis of our experiments and show that some of our model variations outperform the baseline system released for AVSD.
Implicit Knowledge in Argumentative Texts: An Annotated Corpus
Authors: Maria Becker, Katharina Korfhage, Anette Frank
Date: 2019-12-04 00:00:00
Proceeding: LREC 2020 5

When speaking or writing, people omit information that seems clear and evident, such that only part of the message is expressed in words. Especially in argumentative texts it is very common that (important) parts of the argument are implied and omitted. We hypothesize that for argument analysis it will be beneficial to reconstruct this implied information. As a starting point for filling such knowledge gaps, we build a corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. To learn more about the characteristics of both the argumentative texts and the added information, we further annotate the data with semantic clause types and commonsense knowledge relations. The outcome of our work is a carefully de-signed and richly annotated dataset, for which we then provide an in-depth analysis by investigating characteristic distributions and correlations of the assigned labels. We reveal interesting patterns and intersections between the annotation categories and properties of our dataset, which enable insights into the characteristics of both argumentative texts and implicit knowledge in terms of structural features and semantic information. The results of our analysis can help to assist automated argument analysis and can guide the process of revealing implicit information in argumentative texts automatically.
Decomposing predictability: Semantic feature overlap between words and the dynamics of reading for meaning
Authors: Markus J. Hofmann, Mareike A. Kleemann, Andre Roelke, Christian Vorstius, Ralph Radach
Date: 2019-12-06 00:00:00
Tasks: Sentence

The present study uses a computational approach to examine the role of semantic constraints in normal reading. This methodology avoids confounds inherent in conventional measures of predictability, allowing for theoretically deeper accounts of semantic processing. We start from a definition of associations between words based on the significant log likelihood that two words co-occur frequently together in the sentences of a large text corpus. Direct associations between stimulus words were controlled, and semantic feature overlap between prime and target words was manipulated by their common associates. The stimuli consisted of sentences of the form pronoun, verb, article, adjective and noun, followed by a series of closed class words, e. g. "She rides the grey elephant on one of her many exploratory voyages". The results showed that verb-noun overlap reduces single and first fixation durations of the target noun and adjective-noun overlap reduces go-past durations. A dynamic spreading of activation account suggests that associates of the prime words take some time to become activated: The verb can act on the target noun's early eye-movement measures presented three words later, while the adjective is presented immediately prior to the target, which induces sentence re-examination after a difficult adjective-noun semantic integration.
Zero-shot Text Classification With Generative Language Models
Authors: Raul Puri, Bryan Catanzaro
Date: 2019-12-10 00:00:00
Tasks: Classification, General Classification, Language Modeling, Language Modelling, text-classification, Text Classification, Zero-Shot Text Classification

This work investigates the use of natural language to enable zero-shot model adaptation to new tasks. We use text and metadata from social commenting platforms as a source for a simple pretraining task. We then provide the language model with natural language descriptions of classification tasks as input and train it to generate the correct answer in natural language via a language modeling objective. This allows the model to generalize to new classification tasks without the need for multiple multitask classification heads. We show the zero-shot performance of these generative language models, trained with weak supervision, on six benchmark text classification datasets from the torchtext library. Despite no access to training data, we achieve up to a 45% absolute improvement in classification accuracy over random or majority class baselines. These results show that natural language can serve as simple and powerful descriptors for task adaptation. We believe this points the way to new metalearning strategies for text problems.
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings
Authors: Niels van der Heijden, Samira Abnar, Ekaterina Shutova
Date: 2019-12-15 00:00:00
Tasks: Multilingual Word Embeddings, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Sentence, Transfer Learning, Word Embeddings

The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP). Therefore, many recent studies focus on zero-shot transfer learning and joint training across languages to overcome data scarcity for low-resource languages. In this work we (i) perform a comprehensive comparison of state-ofthe-art multilingual word and sentence encoders on the tasks of named entity recognition (NER) and part of speech (POS) tagging; and (ii) propose a new method for creating multilingual contextualized word embeddings, compare it to multiple baselines and show that it performs at or above state-of-theart level in zero-shot transfer settings. Finally, we show that our method allows for better knowledge sharing across languages in a joint training setting.
Tag-less Back-Translation
Authors: Idris Abdulmumin, Bashir Shehu Galadanci, Aliyu Garba
Date: 2019-12-22 00:00:00
Tasks: Domain Adaptation, Machine Translation, NMT, TAG, Translation

An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of the back-translations of the target-side monolingual data. The standard back-translation method has been shown to be unable to efficiently utilize the available huge amount of existing monolingual data because of the inability of translation models to differentiate between the authentic and synthetic parallel data during training. Tagging, or using gates, has been used to enable translation models to distinguish between synthetic and authentic data, improving standard back-translation and also enabling the use of iterative back-translation on language pairs that underperformed using standard back-translation. In this work, we approach back-translation as a domain adaptation problem, eliminating the need for explicit tagging. In the approach -- \emph{tag-less back-translation} -- the synthetic and authentic parallel data are treated as out-of-domain and in-domain data respectively and, through pre-training and fine-tuning, the translation model is shown to be able to learn more efficiently from them during training. Experimental results have shown that the approach outperforms the standard and tagged back-translation approaches on low resource English-Vietnamese and English-German neural machine translation.
Hybrid Machine Learning Models of Classifying Residential Requests for Smart Dispatching
Authors: T. Chen, J. Sun, H. Lin, Y. Liu
Date: 2019-12-22 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, Classification, Clustering, General Classification, Hybrid Machine Learning

This paper presents a hybrid machine learning method of classifying residential requests in natural language to responsible departments that provide timely responses back to residents under the vision of digital government services in smart cities. Residential requests in natural language descriptions cover almost every aspect of a city's daily operation. Hence the responsible departments are fine-grained to even the level of local communities. There are no specific general categories or labels for each request sample. This causes two issues for supervised classification solutions, namely (1) the request sample data is unbalanced and (2) lack of specific labels for training. To solve these issues, we investigate a hybrid machine learning method that generates meta-class labels by means of unsupervised clustering algorithms; applies two-word embedding methods with three classifiers (including two hierarchical classifiers and one residual convolutional neural network); and selects the best performing classifier as the classification result. We demonstrate our approach performing better classification tasks compared to two benchmarking machine learning models, Naive Bayes classifier and a Multiple Layer Perceptron (MLP). In addition, the hierarchical classification method provides insights into the source of classification errors.