pwc-archive/papers-with-abstracts huggingface dataset:
Shapley Homology: Topological Analysis of Sample Influence for Neural Networks
Authors: Kaixuan Zhang, Qinglong Wang, Xue Liu, C. Lee Giles
Date: 2019-10-15 00:00:00
Tasks: BIG-bench Machine Learning, Data Poisoning

Data samples collected for training machine learning models are typically assumed to be independent and identically distributed (iid). Recent research has demonstrated that this assumption can be problematic as it simplifies the manifold of structured data. This has motivated different research areas such as data poisoning, model improvement, and explanation of machine learning models. In this work, we study the influence of a sample on determining the intrinsic topological features of its underlying manifold. We propose the Shapley Homology framework, which provides a quantitative metric for the influence of a sample of the homology of a simplicial complex. By interpreting the influence as a probability measure, we further define an entropy which reflects the complexity of the data manifold. Our empirical studies show that when using the 0-dimensional homology, on neighboring graphs, samples with higher influence scores have more impact on the accuracy of neural networks for determining the graph connectivity and on several regular grammars whose higher entropy values imply more difficulty in being learned.
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
Authors: Yao Liu, Pierre-Luc Bacon, Emma Brunskill
Date: 2019-10-15 00:00:00
Proceeding: ICML 2020 1
Tasks: Off-policy evaluation, Reinforcement Learning

Off-policy policy estimators that use importance sampling (IS) can suffer from high variance in long-horizon domains, and there has been particular excitement over new IS methods that leverage the structure of Markov decision processes. We analyze the variance of the most popular approaches through the viewpoint of conditional Monte Carlo. Surprisingly, we find that in finite horizon MDPs there is no strict variance reduction of per-decision importance sampling or stationary importance sampling, comparing with vanilla importance sampling. We then provide sufficient conditions under which the per-decision or stationary estimators will provably reduce the variance over importance sampling with finite horizons. For the asymptotic (in terms of horizon $T$) case, we develop upper and lower bounds on the variance of those estimators which yields sufficient conditions under which there exists an exponential v.s. polynomial gap between the variance of importance sampling and that of the per-decision or stationary estimators. These results help advance our understanding of if and when new types of IS estimators will improve the accuracy of off-policy estimation.
DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage
Authors: Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang
Date: 2019-10-15 00:00:00
Tasks: Code Completion

The source code suggestions provided by current IDEs are mostly dependent on static type learning. These suggestions often end up proposing irrelevant suggestions for a peculiar context. Recently, deep learning-based approaches have shown great potential in the modeling of source code for various software engineering tasks. However, these techniques lack adequate generalization and resistance to acclimate the use of such models in a real-world software development environment. This letter presents \textit{DeepVS}, an end-to-end deep neural code completion tool that learns from existing codebases by exploiting the bidirectional Gated Recurrent Unit (BiGRU) neural net. The proposed tool is capable of providing source code suggestions instantly in an IDE by using pre-trained BiGRU neural net. The evaluation of this work is two-fold, quantitative and qualitative. Through extensive evaluation on ten real-world open-source software systems, the proposed method shows significant performance enhancement and its practicality. Moreover, the results also suggest that \textit{DeepVS} tool is capable of suggesting zero-day (unseen) code tokens by learning coding patterns from real-world software systems.
Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction
Authors: Shaika Chowdhury, Chenwei Zhang, Philip S. Yu, Yuan Luo
Date: 2019-10-15 00:00:00
Tasks: Mortality Prediction, Semantic correspondence, Sentence

Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients. Electronic health notes serve as a rich source for learning patient representations, that can facilitate effective risk assessment. However, a large portion of clinical notes are unstructured and also contain domain specific terminologies, from which we need to extract structured information. In this paper, we introduce an embedding framework to learn semantically-plausible distributed representations of clinical notes that exploits the semantic correspondence between the unstructured texts and their corresponding structured knowledge, known as semantic frame, in a hierarchical fashion. Our approach integrates text modeling and semantic correspondence learning into a single model that comprises 1) an unstructured embedding module that makes use of self-similarity matrix representations in order to inject structural regularities of different segments inherent in clinical texts to promote local coherence, 2) a structured embedding module to embed the semantic frames (e.g., UMLS semantic types) with deep ConvNet and 3) a hierarchical semantic correspondence module that embeds by enhancing the interactions between text-semantic frame embedding pairs at multiple levels (i.e., words, sentence, note). Evaluations on multiple embedding benchmarks on post discharge intensive care patient mortality prediction tasks demonstrate its effectiveness compared to approaches that do not exploit the semantic interactions between structured and unstructured information present in clinical notes.
Reinforcement learning with a network of spiking agents
Authors: Sneha Aenugu, Abhishek Sharma, Sasikiran Yelamarthi, Hananel Hazan, Philip S. Thomas, Robert Kozma
Date: 2019-10-15 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions. We build on this theory to propose a multi-agent learning framework with spiking neurons in the generalized linear model (GLM) formulation as agents, to solve reinforcement learning (RL) tasks. We show that a network of GLM spiking agents connected in a hierarchical fashion, where each spiking agent modulates its firing policy based on local information and a global prediction error, can learn complex action representations to solve RL tasks. We further show how leveraging principles of modularity and population coding inspired from the brain can help reduce variance in the learning updates making it a viable optimization technique.
Exploring Overall Contextual Information for Image Captioning in Human-Like Cognitive Style
Authors: Hongwei Ge, Zehang Yan, Kai Zhang, Mingde Zhao, Liang Sun
Date: 2019-10-15 00:00:00
Proceeding: ICCV 2019 10
Tasks: Decoder, Image Captioning, Sentence

Image captioning is a research hotspot where encoder-decoder models combining convolutional neural network (CNN) and long short-term memory (LSTM) achieve promising results. Despite significant progress, these models generate sentences differently from human cognitive styles. Existing models often generate a complete sentence from the first word to the end, without considering the influence of the following words on the whole sentence generation. In this paper, we explore the utilization of a human-like cognitive style, i.e., building overall cognition for the image to be described and the sentence to be constructed, for enhancing computer image understanding. This paper first proposes a Mutual-aid network structure with Bidirectional LSTMs (MaBi-LSTMs) for acquiring overall contextual information. In the training process, the forward and backward LSTMs encode the succeeding and preceding words into their respective hidden states by simultaneously constructing the whole sentence in a complementary manner. In the captioning process, the LSTM implicitly utilizes the subsequent semantic information contained in its hidden states. In fact, MaBi-LSTMs can generate two sentences in forward and backward directions. To bridge the gap between cross-domain models and generate a sentence with higher quality, we further develop a cross-modal attention mechanism to retouch the two sentences by fusing their salient parts as well as the salient areas of the image. Experimental results on the Microsoft COCO dataset show that the proposed model improves the performance of encoder-decoder models and achieves state-of-the-art results.
End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Authors: Jinzheng Cai, Yingda Xia, Dong Yang, Daguang Xu, Lin Yang, Holger Roth
Date: 2019-10-15 00:00:00
Tasks: Organ Segmentation, Pancreas Segmentation, Segmentation

Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture -- organ point-network. It takes deep learning features as inputs and generates organ shape representations as points that located on organ surface. We later present a novel adversarial shape learning objective function to optimize the point-network to capture shape information better. We train the point-network together with a CNN-based segmentation model in a multi-task fashion so that the shared network parameters can benefit from both shape learning and segmentation tasks. We demonstrate our method with three challenging abdomen organs including liver, spleen, and pancreas. The point-network generates surface points with fine-grained details and it is found critical for improving organ segmentation. Consequently, the deep segmentation model is improved by the introduced shape learning as significantly better Dice scores are observed for spleen and pancreas segmentation.
State of Compact Architecture Search For Deep Neural Networks
Authors: Mohammad Javad Shafiee, Andrew Hryniowski, Francis Li, Zhong Qiu Lin, Alexander Wong
Date: 2019-10-15 00:00:00

The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge and mobile scenarios. Due to the time-consuming and challenging nature of manually designing compact deep neural networks, there has been significant recent research interest into algorithms that automatically search for compact network architectures. A particularly interesting class of compact architecture search algorithms are those that are guided by baseline network architectures. Such algorithms have been shown to be significantly more computationally efficient than unguided methods. In this study, we explore the current state of compact architecture search for deep neural networks through both theoretical and empirical analysis of four different state-of-the-art compact architecture search algorithms: i) group lasso regularization, ii) variational dropout, iii) MorphNet, and iv) Generative Synthesis. We examine these methods in detail based on a number of different factors such as efficiency, effectiveness, and scalability. Furthermore, empirical evaluations are conducted to compare the efficacy of these compact architecture search algorithms across three well-known benchmark datasets. While by no means an exhaustive exploration, we hope that this study helps provide insights into the interesting state of this relatively new area of research in terms of diversity and real, tangible gains already achieved in architecture design improvements. Furthermore, the hope is that this study would help in pushing the conversation forward towards a deeper theoretical and empirical understanding where the research community currently stands in the landscape of compact architecture search for deep neural networks, and the practical challenges and considerations in leveraging such approaches for operational usage.
Low Bit-Rate Speech Coding with VQ-VAE and a WaveNet Decoder
Authors: Cristina Gârbacea, Aäron van den Oord, Yazhe Li, Felicia S. C. Lim, Alejandro Luebs, Oriol Vinyals, Thomas C. Walters
Date: 2019-10-14 00:00:00
Tasks: Decoder

In order to efficiently transmit and store speech signals, speech codecs create a minimally redundant representation of the input signal which is then decoded at the receiver with the best possible perceptual quality. In this work we demonstrate that a neural network architecture based on VQ-VAE with a WaveNet decoder can be used to perform very low bit-rate speech coding with high reconstruction quality. A prosody-transparent and speaker-independent model trained on the LibriSpeech corpus coding audio at 1.6 kbps exhibits perceptual quality which is around halfway between the MELP codec at 2.4 kbps and AMR-WB codec at 23.05 kbps. In addition, when training on high-quality recorded speech with the test speaker included in the training set, a model coding speech at 1.6 kbps produces output of similar perceptual quality to that generated by AMR-WB at 23.05 kbps.
TCD-NPE: A Re-configurable and Efficient Neural Processing Engine, Powered by Novel Temporal-Carry-deferring MACs
Authors: Ali Mirzaeian, Houman Homayoun, Avesta Sasan
Date: 2019-10-14 00:00:00

In this paper, we first propose the design of Temporal-Carry-deferring MAC (TCD-MAC) and illustrate how our proposed solution can gain significant energy and performance benefit when utilized to process a stream of input data. We then propose using the TCD-MAC to build a reconfigurable, high speed, and low power Neural Processing Engine (TCD-NPE). We, further, propose a novel scheduler that lists the sequence of needed processing events to process an MLP model in the least number of computational rounds in our proposed TCD-NPE. We illustrate that our proposed TCD-NPE significantly outperform similar neural processing solutions that use conventional MACs in terms of both energy consumption and execution time.
Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare
Authors: Shaika Chowdhury, Chenwei Zhang, Philip S. Yu, Yuan Luo
Date: 2019-10-14 00:00:00
Tasks: Decoder, Representation Learning

Distributed representations have been used to support downstream tasks in healthcare recently. Healthcare data (e.g., electronic health records) contain multiple modalities of data from heterogeneous sources that can provide complementary information, alongside an added dimension to learning personalized patient representations. To this end, in this paper we propose a novel unsupervised encoder-decoder model, namely Mixed Pooling Multi-View Attention Autoencoder (MPVAA), that generates patient representations encapsulating a holistic view of their medical profile. Specifically, by first learning personalized graph embeddings pertaining to each patient's heterogeneous healthcare data, it then integrates the non-linear relationships among them into a unified representation through multi-view attention mechanism. Additionally, a mixed pooling strategy is incorporated in the encoding step to learn diverse information specific to each data modality. Experiments conducted for multiple tasks demonstrate the effectiveness of the proposed model over the state-of-the-art representation learning methods in healthcare.
Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks
Authors: Joseph Z. Xu, Wenhan Lu, Zebo Li, Pranav Khaitan, Valeriya Zaytseva
Date: 2019-10-14 00:00:00
Tasks: BIG-bench Machine Learning

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We also quantify how well the models will generalize to future disasters by training and testing models on different disaster events.
Whatcha lookin' at? DeepLIFTing BERT's Attention in Question Answering
Authors: Ekaterina Arkhangelskaia, Sourav Dutta
Date: 2019-10-14 00:00:00
Tasks: Question Answering

There has been great success recently in tackling challenging NLP tasks by neural networks which have been pre-trained and fine-tuned on large amounts of task data. In this paper, we investigate one such model, BERT for question-answering, with the aim to analyze why it is able to achieve significantly better results than other models. We run DeepLIFT on the model predictions and test the outcomes to monitor shift in the attention values for input. We also cluster the results to analyze any possible patterns similar to human reasoning depending on the kind of input paragraph and question the model is trying to answer.
Restoration of marker occluded hematoxylin and eosin stained whole slide histology images using generative adversarial networks
Authors: Bairavi Venkatesh, Tosha Shah, Antong Chen, Soheil Ghafurian
Date: 2019-10-14 00:00:00
Tasks: Generative Adversarial Network, Image-to-Image Translation, Translation, whole slide images

It is common for pathologists to annotate specific regions of the tissue, such as tumor, directly on the glass slide with markers. Although this practice was helpful prior to the advent of histology whole slide digitization, it often occludes important details which are increasingly relevant to immuno-oncology due to recent advancements in digital pathology imaging techniques. The current work uses a generative adversarial network with cycle loss to remove these annotations while still maintaining the underlying structure of the tissue by solving an image-to-image translation problem. We train our network on up to 300 whole slide images with marker inks and show that 70% of the corrected image patches are indistinguishable from originally uncontaminated image tissue to a human expert. This portion increases 97% when we replace the human expert with a deep residual network. We demonstrated the fidelity of the method to the original image by calculating the correlation between image gradient magnitudes. We observed a revival of up to 94,000 nuclei per slide in our dataset, the majority of which were located on tissue border.
Tell-the-difference: Fine-grained Visual Descriptor via a Discriminating Referee
Authors: Shuangjie Xu, Feng Xu, Yu Cheng, Pan Zhou
Date: 2019-10-14 00:00:00
Tasks: Decoder, Image Captioning

In this paper, we investigate a novel problem of telling the difference between image pairs in natural language. Compared to previous approaches for single image captioning, it is challenging to fetch linguistic representation from two independent visual information. To this end, we have proposed an effective encoder-decoder caption framework based on Hyper Convolution Net. In addition, a series of novel feature fusing techniques for pairwise visual information fusing are introduced and a discriminating referee is proposed to evaluate the pipeline. Because of the lack of appropriate datasets to support this task, we have collected and annotated a large new dataset with Amazon Mechanical Turk (AMT) for generating captions in a pairwise manner (with 14764 images and 26710 image pairs in total). The dataset is the first one on the relative difference caption task that provides descriptions in free language. We evaluate the effectiveness of our model on two datasets in the field and it outperforms the state-of-the-art approach by a large margin.
Real-time Data Driven Precision Estimator for RAVEN-II Surgical Robot End Effector Position
Authors: Haonan Peng, Xingjian Yang, Yun-Hsuan Su, Blake Hannaford
Date: 2019-10-14 00:00:00
Tasks: Position

Surgical robots have been introduced to operating rooms over the past few decades due to their high sensitivity, small size, and remote controllability. The cable-driven nature of many surgical robots allows the systems to be dexterous and lightweight, with diameters as low as 5mm. However, due to the slack and stretch of the cables and the backlash of the gears, inevitable uncertainties are brought into the kinematics calculation. Since the reported end effector position of surgical robots like RAVEN-II is directly calculated using the motor encoder measurements and forward kinematics, it may contain relatively large error up to 10mm, whereas semi-autonomous functions being introduced into abdominal surgeries require position inaccuracy of at most 1mm. To resolve the problem, a cost-effective, real-time and data-driven pipeline for robot end effector position precision estimation is proposed and tested on RAVEN-II. Analysis shows an improved end effector position error of around 1mm RMS traversing through the entire robot workspace without high-resolution motion tracker.
A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme
Authors: Paavo Parmas, Masashi Sugiyama
Date: 2019-10-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reparameterization (RP) and likelihood ratio (LR) gradient estimators are used throughout machine and reinforcement learning; however, they are usually explained as simple mathematical tricks without providing any insight into their nature. We use a first principles approach to explain LR and RP, and show a connection between the two via the divergence theorem. The theory motivated us to derive optimal importance sampling schemes to reduce LR gradient variance. Our newly derived distributions have analytic probability densities and can be directly sampled from. The improvement for Gaussian target distributions was modest, but for other distributions such as a Beta distribution, our method could lead to arbitrarily large improvements, and was crucial to obtain competitive performance in evolution strategies experiments.
Mapping Supervised Bilingual Word Embeddings from English to low-resource languages
Authors: Sourav Dutta
Date: 2019-10-14 00:00:00
Tasks: Machine Translation, Retrieval, Translation, Word Embeddings

It is very challenging to work with low-resource languages due to the inadequate availability of data. Using a dictionary to map independently trained word embeddings into a shared vector space has proved to be very useful in learning bilingual embeddings in the past. Here we have tried to map individual embeddings of words in English and their corresponding translated words in low-resource languages like Estonian, Slovenian, Slovakian, and Hungarian. We have used a supervised learning approach. We report accuracy scores through various retrieval strategies which show that it is possible to approach challenging tasks in Natural Language Processing like machine translation for such languages, provided that we have at least some amount of proper bilingual data. We also conclude that we can follow an unsupervised learning path on monolingual text data as that is more suitable for low-resource languages.