pwc-archive/papers-with-abstracts huggingface dataset:
Multi-hop Question Answering via Reasoning Chains
Authors: Jifan Chen, Shih-ting Lin, Greg Durrett
Date: 2019-10-07 00:00:00
Tasks: Coreference Resolution, Multi-hop Question Answering, Named Entity Recognition, Named Entity Recognition (NER), Question Answering, Sentence

Multi-hop question answering requires models to gather information from different parts of a text to answer a question. Most current approaches learn to address this task in an end-to-end way with neural networks, without maintaining an explicit representation of the reasoning process. We propose a method to extract a discrete reasoning chain over the text, which consists of a series of sentences leading to the answer. We then feed the extracted chains to a BERT-based QA model to do final answer prediction. Critically, we do not rely on gold annotated chains or "supporting facts:" at training time, we derive pseudogold reasoning chains using heuristics based on named entity recognition and coreference resolution. Nor do we rely on these annotations at test time, as our model learns to extract chains from raw text alone. We test our approach on two recently proposed large multi-hop question answering datasets: WikiHop and HotpotQA, and achieve state-of-art performance on WikiHop and strong performance on HotpotQA. Our analysis shows the properties of chains that are crucial for high performance: in particular, modeling extraction sequentially is important, as is dealing with each candidate sentence in a context-aware way. Furthermore, human evaluation shows that our extracted chains allow humans to give answers with high confidence, indicating that these are a strong intermediate abstraction for this task.
Human Action Sequence Classification
Authors: Yan Bin Ng, Basura Fernando
Date: 2019-10-07 00:00:00
Tasks: Action Classification, Action Localization, Action Segmentation, Classification, General Classification, Machine Translation, Translation, Video Captioning

This paper classifies human action sequences from videos using a machine translation model. In contrast to classical human action classification which outputs a set of actions, our method output a sequence of action in the chronological order of the actions performed by the human. Therefore our method is evaluated using sequential performance measures such as Bilingual Evaluation Understudy (BLEU) scores. Action sequence classification has many applications such as learning from demonstration, action segmentation, detection, localization and video captioning. Furthermore, we use our model that is trained to output action sequences to solve downstream tasks; such as video captioning and action localization. We obtain state of the art results for video captioning in challenging Charades dataset obtaining BLEU-4 score of 34.8 and METEOR score of 33.6 outperforming previous state-of-the-art of 18.8 and 19.5 respectively. Similarly, on ActivityNet captioning, we obtain excellent results in-terms of ROUGE (20.24) and CIDER (37.58) scores. For action localization, without using any explicit start/end action annotations, our method obtains localization performance of 22.2 mAP outperforming prior fully supervised methods.
Deep Evidential Regression
Authors: Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus
Date: 2019-10-07 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: regression, Uncertainty Quantification

Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper, we propose a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order to learn both aleatoric and epistemic uncertainty. We accomplish this by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution. We additionally impose priors during training such that the model is regularized when its predicted evidence is not aligned with the correct output. Our method does not rely on sampling during inference or on out-of-distribution (OOD) examples for training, thus enabling efficient and scalable uncertainty learning. We demonstrate learning well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, as well as robustness to adversarial and OOD test samples.
Weighted graphlets and deep neural networks for protein structure classification
Authors: Hongyu Guo, Khalique Newaz, Scott Emrich, Tijana Milenkovic, Jun Li
Date: 2019-10-07 00:00:00
Tasks: Classification, General Classification

As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem of protein structure classification, which computationally classifies the structures of proteins into pre-defined groups. We develop a weighted network that depicts the protein structures, and more importantly, we propose the first graphlet-based measure that applies to weighted networks. Further, we develop a deep neural network (DNN) composed of both convolutional and recurrent layers to use this measure for classification. Put together, our approach shows dramatic improvements in performance over existing graphlet-based approaches on 36 real datasets. Even comparing with the state-of-the-art approach, it almost halves the classification error. In addition to protein structure networks, our weighted-graphlet measure and DNN classifier can potentially be applied to classification of other weighted networks in computational biology as well as in other domains.
Unsupervised Image Super-Resolution with an Indirect Supervised Path
Authors: Zhen Han, Enyan Dai, Xu Jia, Xiaoying Ren, Shuaijun Chen, Chunjing Xu, Jianzhuang Liu, Qi Tian
Date: 2019-10-07 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, Translation

The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image. Although significant progress has been made by deep learning models, they are trained on synthetic paired data in a supervised way and do not perform well on real data. There are several attempts that directly apply unsupervised image translation models to address such a problem. However, unsupervised low-level vision problem poses more challenge on the accuracy of translation. In this work,we propose a novel framework which is composed of two stages: 1) unsupervised image translation between real LR images and synthetic LR images; 2) supervised super-resolution from approximated real LR images to HR images. It takes the synthetic LR images as a bridge and creates an indirect supervised path from real LR images to HR images. Any existed deep learning based image super-resolution model can be integrated into the second stage of the proposed framework for further improvement. In addition it shows great flexibility in balancing between distortion and perceptual quality under unsupervised setting. The proposed method is evaluated on both NTIRE 2017 and 2018 challenge datasets and achieves favorable performance against supervised methods.
Why Attention? Analyzing and Remedying BiLSTM Deficiency in Modeling Cross-Context for NER
Authors: Peng-Hsuan Li, Tsu-Jui Fu, Wei-Yun Ma
Date: 2019-10-07 00:00:00
Tasks: NER

State-of-the-art approaches of NER have used sequence-labeling BiLSTM as a core module. This paper formally shows the limitation of BiLSTM in modeling cross-context patterns. Two types of simple cross-structures -- self-attention and Cross-BiLSTM -- are shown to effectively remedy the problem. On both OntoNotes 5.0 and WNUT 2017, clear and consistent improvements are achieved over bare-bone models, up to 8.7% on some of the multi-token mentions. In-depth analyses across several aspects of the improvements, especially the identification of multi-token mentions, are further given.
A Novel Technique of Noninvasive Hemoglobin Level Measurement Using HSV Value of Fingertip Image
Authors: Md. Kamrul Hasan, Nazmus Sakib, Joshua Field, Richard R. Love, Sheikh I. Ahamed
Date: 2019-10-07 00:00:00
Tasks: Cloud Computing

Over the last decade, smartphones have changed radically to support us with mHealth technology, cloud computing, and machine learning algorithm. Having its multifaceted facilities, we present a novel smartphone-based noninvasive hemoglobin (Hb) level prediction model by analyzing hue, saturation and value (HSV) of a fingertip video. Here, we collect 60 videos of 60 subjects from two different locations: Blood Center of Wisconsin, USA and AmaderGram, Bangladesh. We extract red, green, and blue (RGB) pixel intensities of selected images of those videos captured by the smartphone camera with flash on. Then we convert RGB values of selected video frames of a fingertip video into HSV color space and we generate histogram values of these HSV pixel intensities. We average these histogram values of a fingertip video and consider as an observation against the gold standard Hb concentration. We generate two input feature matrices based on observation of two different data sets. Partial Least Squares (PLS) algorithm is applied on the input feature matrix. We observe R2=0.95 in both data sets through our research. We analyze our data using Python OpenCV, Matlab, and R statistics tool.
Differential Privacy-enabled Federated Learning for Sensitive Health Data
Authors: Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, Amar Das
Date: 2019-10-07 00:00:00
Tasks: Federated Learning

Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure. In this paper, we introduce a federated learning framework that can learn a global model from distributed health data held locally at different sites. The framework offers two levels of privacy protection. First, it does not move or share raw data across sites or with a centralized server during the model training process. Second, it uses a differential privacy mechanism to further protect the model from potential privacy attacks. We perform a comprehensive evaluation of our approach on two healthcare applications, using real-world electronic health data of 1 million patients. We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.
PyODDS: An End-to-End Outlier Detection System
Authors: Yuening Li, Daochen Zha, Na Zou, Xia Hu
Date: 2019-10-07 00:00:00
Tasks: BIG-bench Machine Learning, Outlier Detection

PyODDS is an end-to end Python system for outlier detection with database support. PyODDS provides outlier detection algorithms which meet the demands for users in different fields, w/wo data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without moving data out of the database server or over the network. It also provides access to a wide range of outlier detection algorithms, including statistical analysis and more recent deep learning based approaches. PyODDS is released under the MIT open-source license, and currently available at (https://github.com/datamllab/pyodds) with official documentations at (https://pyodds.github.io/).
Representation Learning of EHR Data via Graph-Based Medical Entity Embedding
Authors: Tong Wu, Yunlong Wang, Yue Wang, Emily Zhao, Yilian Yuan, Zhi Yang
Date: 2019-10-07 00:00:00
Tasks: Graph Embedding, Representation Learning

Automatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare informatics that turns heterogeneous medical records into structured and actionable information. Here we propose ME2Vec, an algorithmic framework for learning low-dimensional vectors of the most common entities in EHR: medical services, doctors, and patients. ME2Vec leverages diverse graph embedding techniques to cater for the unique characteristic of each medical entity. Using real-world clinical data, we demonstrate the efficacy of ME2Vec over competitive baselines on disease diagnosis prediction.
Gaussian Mixture Clustering Using Relative Tests of Fit
Authors: Purvasha Chakravarti, Sivaraman Balakrishnan, Larry Wasserman
Date: 2019-10-07 00:00:00
Tasks: Clustering, Model Selection

We consider clustering based on significance tests for Gaussian Mixture Models (GMMs). Our starting point is the SigClust method developed by Liu et al. (2008), which introduces a test based on the k-means objective (with k = 2) to decide whether the data should be split into two clusters. When applied recursively, this test yields a method for hierarchical clustering that is equipped with a significance guarantee. We study the limiting distribution and power of this approach in some examples and show that there are large regions of the parameter space where the power is low. We then introduce a new test based on the idea of relative fit. Unlike prior work, we test for whether a mixture of Gaussians provides a better fit relative to a single Gaussian, without assuming that either model is correct. The proposed test has a simple critical value and provides provable error control. One version of our test provides exact, finite sample control of the type I error. We show how our tests can be used for hierarchical clustering as well as in a sequential manner for model selection. We conclude with an extensive simulation study and a cluster analysis of a gene expression dataset.
Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study
Authors: Manuel Serra Nunes, Atabak Dehban, Plinio Moreno, José Santos-Victor
Date: 2019-10-07 00:00:00
Tasks: Benchmarking, Prediction, Video Prediction

A defining characteristic of intelligent systems is the ability to make action decisions based on the anticipated outcomes. Video prediction systems have been demonstrated as a solution for predicting how the future will unfold visually, and thus, many models have been proposed that are capable of predicting future frames based on a history of observed frames~(and sometimes robot actions). However, a comprehensive method for determining the fitness of different video prediction models at guiding the selection of actions is yet to be developed. Current metrics assess video prediction models based on human perception of frame quality. In contrast, we argue that if these systems are to be used to guide action, necessarily, the actions the robot performs should be encoded in the predicted frames. In this paper, we are proposing a new metric to compare different video prediction models based on this argument. More specifically, we propose an action inference system and quantitatively rank different models based on how well we can infer the robot actions from the predicted frames. Our extensive experiments show that models with high perceptual scores can perform poorly in the proposed action inference tests and thus, may not be suitable options to be used in robot planning systems.
MASTER: Multi-Aspect Non-local Network for Scene Text Recognition
Authors: Ning Lu, Wenwen Yu, Xianbiao Qi, Yihao Chen, Ping Gong, Rong Xiao, Xiang Bai
Date: 2019-10-07 00:00:00
Tasks: Decoder, Scene Text Recognition

Attention-based scene text recognizers have gained huge success, which leverages a more compact intermediate representation to learn 1d- or 2d- attention by a RNN-based encoder-decoder architecture. However, such methods suffer from attention-drift problem because high similarity among encoded features leads to attention confusion under the RNN-based local attention mechanism. Moreover, RNN-based methods have low efficiency due to poor parallelization. To overcome these problems, we propose the MASTER, a self-attention based scene text recognizer that (1) not only encodes the input-output attention but also learns self-attention which encodes feature-feature and target-target relationships inside the encoder and decoder and (2) learns a more powerful and robust intermediate representation to spatial distortion, and (3) owns a great training efficiency because of high training parallelization and a high-speed inference because of an efficient memory-cache mechanism. Extensive experiments on various benchmarks demonstrate the superior performance of our MASTER on both regular and irregular scene text. Pytorch code can be found at https://github.com/wenwenyu/MASTER-pytorch, and Tensorflow code can be found at https://github.com/jiangxiluning/MASTER-TF.
Domain Differential Adaptation for Neural Machine Translation
Authors: Zi-Yi Dou, Xinyi Wang, Junjie Hu, Graham Neubig
Date: 2019-10-07 00:00:00
Proceeding: WS 2019 11
Tasks: Domain Adaptation, Machine Translation, Translation

Neural networks are known to be data hungry and domain sensitive, but it is nearly impossible to obtain large quantities of labeled data for every domain we are interested in. This necessitates the use of domain adaptation strategies. One common strategy encourages generalization by aligning the global distribution statistics between source and target domains, but one drawback is that the statistics of different domains or tasks are inherently divergent, and smoothing over these differences can lead to sub-optimal performance. In this paper, we propose the framework of {\it Domain Differential Adaptation (DDA)}, where instead of smoothing over these differences we embrace them, directly modeling the difference between domains using models in a related task. We then use these learned domain differentials to adapt models for the target task accordingly. Experimental results on domain adaptation for neural machine translation demonstrate the effectiveness of this strategy, achieving consistent improvements over other alternative adaptation strategies in multiple experimental settings.
Soft-Label Dataset Distillation and Text Dataset Distillation
Authors: Ilia Sucholutsky, Matthias Schonlau
Date: 2019-10-06 00:00:00
Tasks: Dataset Distillation, Data Summarization, image-classification, Image Classification, Sentiment Analysis

Dataset distillation is a method for reducing dataset sizes by learning a small number of synthetic samples containing all the information of a large dataset. This has several benefits like speeding up model training, reducing energy consumption, and reducing required storage space. Currently, each synthetic sample is assigned a single `hard' label, and also, dataset distillation can currently only be used with image data. We propose to simultaneously distill both images and their labels, thus assigning each synthetic sample a `soft' label (a distribution of labels). Our algorithm increases accuracy by 2-4% over the original algorithm for several image classification tasks. Using `soft' labels also enables distilled datasets to consist of fewer samples than there are classes as each sample can encode information for multiple classes. For example, training a LeNet model with 10 distilled images (one per class) results in over 96% accuracy on MNIST, and almost 92% accuracy when trained on just 5 distilled images. We also extend the dataset distillation algorithm to distill sequential datasets including texts. We demonstrate that text distillation outperforms other methods across multiple datasets. For example, models attain almost their original accuracy on the IMDB sentiment analysis task using just 20 distilled sentences. Our code can be found at $\href{https://github.com/ilia10000/dataset-distillation}{\text{https://github.com/ilia10000/dataset-distillation}}$.
ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation
Authors: Shreeyak S. Sajjan, Matthew Moore, Mike Pan, Ganesh Nagaraja, Johnny Lee, Andy Zeng, Shuran Song
Date: 2019-10-06 00:00:00
Tasks: 3D geometry, Depth Completion, Depth Estimation, Monocular Depth Estimation, Semantic Segmentation, Surface Normals Estimation, Transparent Object Depth Estimation, Transparent Object Detection, Transparent objects

Transparent objects are a common part of everyday life, yet they possess unique visual properties that make them incredibly difficult for standard 3D sensors to produce accurate depth estimates for. In many cases, they often appear as noisy or distorted approximations of the surfaces that lie behind them. To address these challenges, we present ClearGrasp -- a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation. Given a single RGB-D image of transparent objects, ClearGrasp uses deep convolutional networks to infer surface normals, masks of transparent surfaces, and occlusion boundaries. It then uses these outputs to refine the initial depth estimates for all transparent surfaces in the scene. To train and test ClearGrasp, we construct a large-scale synthetic dataset of over 50,000 RGB-D images, as well as a real-world test benchmark with 286 RGB-D images of transparent objects and their ground truth geometries. The experiments demonstrate that ClearGrasp is substantially better than monocular depth estimation baselines and is capable of generalizing to real-world images and novel objects. We also demonstrate that ClearGrasp can be applied out-of-the-box to improve grasping algorithms' performance on transparent objects. Code, data, and benchmarks will be released. Supplementary materials available on the project website: https://sites.google.com/view/cleargrasp
Rethinking Kernel Methods for Node Representation Learning on Graphs
Authors: Yu Tian, Long Zhao, Xi Peng, Dimitris N. Metaxas
Date: 2019-10-06 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Classification, General Classification, Graph Classification, Graph Representation Learning, Graph Similarity, Link Prediction, Node Classification, Representation Learning

Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which is a related problem to graph representation learning, is still ill-posed and the state-of-the-art methods are heavily based on heuristics. Here, we present a novel theoretical kernel-based framework for node classification that can bridge the gap between these two representation learning problems on graphs. Our approach is motivated by graph kernel methodology but extended to learn the node representations capturing the structural information in a graph. We theoretically show that our formulation is as powerful as any positive semidefinite kernels. To efficiently learn the kernel, we propose a novel mechanism for node feature aggregation and a data-driven similarity metric employed during the training phase. More importantly, our framework is flexible and complementary to other graph-based deep learning models, e.g., Graph Convolutional Networks (GCNs). We empirically evaluate our approach on a number of standard node classification benchmarks, and demonstrate that our model sets the new state of the art.