July 30, 2019

3391 words 16 mins read

Paper Group AWR 41

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. Kernalised Multi-resolution Convnet for Visual Tracking. Neural Question Generation from Text: A Preliminary Study. An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models. Towards Principled Methods for Training …

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis


Title	Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
Authors	Benjamin Shickel, Patrick Tighe, Azra Bihorac, Parisa Rashidi
Abstract	The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHR). While primarily designed for archiving patient clinical information and administrative healthcare tasks, many researchers have found secondary use of these records for various clinical informatics tasks. Over the same period, the machine learning community has seen widespread advances in deep learning techniques, which also have been successfully applied to the vast amount of EHR data. In this paper, we review these deep EHR systems, examining architectures, technical aspects, and clinical applications. We also identify shortcomings of current techniques and discuss avenues of future research for EHR-based deep learning.
Tasks
Published	2017-06-12
URL	http://arxiv.org/abs/1706.03446v2
PDF	http://arxiv.org/pdf/1706.03446v2.pdf
PWC	https://paperswithcode.com/paper/deep-ehr-a-survey-of-recent-advances-in-deep
Repo	https://github.com/cyrilmaxwell/MedicalAIPaperList
Framework	none

Kernalised Multi-resolution Convnet for Visual Tracking


Title	Kernalised Multi-resolution Convnet for Visual Tracking
Authors	Di Wu, Wenbin Zou, Xia Li, Yong Zhao
Abstract	Visual tracking is intrinsically a temporal problem. Discriminative Correlation Filters (DCF) have demonstrated excellent performance for high-speed generic visual object tracking. Built upon their seminal work, there has been a plethora of recent improvements relying on convolutional neural network (CNN) pretrained on ImageNet as a feature extractor for visual tracking. However, most of their works relying on ad hoc analysis to design the weights for different layers either using boosting or hedging techniques as an ensemble tracker. In this paper, we go beyond the conventional DCF framework and propose a Kernalised Multi-resolution Convnet (KMC) formulation that utilises hierarchical response maps to directly output the target movement. When directly deployed the learnt network to predict the unseen challenging UAV tracking dataset without any weight adjustment, the proposed model consistently achieves excellent tracking performance. Moreover, the transfered multi-reslution CNN renders it possible to be integrated into the RNN temporal learning framework, therefore opening the door on the end-to-end temporal deep learning (TDL) for visual tracking.
Tasks	Object Tracking, Visual Object Tracking, Visual Tracking
Published	2017-08-02
URL	http://arxiv.org/abs/1708.00577v1
PDF	http://arxiv.org/pdf/1708.00577v1.pdf
PWC	https://paperswithcode.com/paper/kernalised-multi-resolution-convnet-for
Repo	https://github.com/stevenwudi/KMC_cvprw_2017
Framework	tf

Neural Question Generation from Text: A Preliminary Study


Title	Neural Question Generation from Text: A Preliminary Study
Authors	Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, Ming Zhou
Abstract	Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions.
Tasks	Question Generation
Published	2017-04-06
URL	http://arxiv.org/abs/1704.01792v3
PDF	http://arxiv.org/pdf/1704.01792v3.pdf
PWC	https://paperswithcode.com/paper/neural-question-generation-from-text-a
Repo	https://github.com/magic282/NQG
Framework	pytorch

An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models


Title	An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models
Authors	Peter Henderson, Matthew Vertescher
Abstract	Motion detection in video is important for a number of applications and fields. In video surveillance, motion detection is an essential accompaniment to activity recognition for early warning systems. Robotics also has much to gain from motion detection and segmentation, particularly in high speed motion tracking for tactile systems. There are a myriad of techniques for detecting and masking motion in an image. Successful systems have used Gaussian Models to discern background from foreground in an image (motion from static imagery). However, particularly in the case of a moving camera or frame of reference, it is necessary to compensate for the motion of the camera when attempting to discern objects moving in the foreground. For example, it is possible to estimate motion of the camera through optical flow methods or temporal differencing and then compensate for this motion in a background subtraction model. We selection a method by Yi et al. using Dual-Mode Single Gaussian Models which does just this. We implement the technique in Intel’s Thread Building Blocks (TBB) and NVIDIA’s CUDA libraries. We then compare parallelization improvements with a theoretical analysis of speedups based on the characteristics of our selected model and attributes of both TBB and CUDA. We make our implementation available to the public.
Tasks	Activity Recognition, Motion Detection, Optical Flow Estimation
Published	2017-02-16
URL	http://arxiv.org/abs/1702.05156v1
PDF	http://arxiv.org/pdf/1702.05156v1.pdf
PWC	https://paperswithcode.com/paper/an-analysis-of-parallelized-motion-masking
Repo	https://github.com/Breakend/MotionDetection
Framework	none

Towards Principled Methods for Training Generative Adversarial Networks


Title	Towards Principled Methods for Training Generative Adversarial Networks
Authors	Martin Arjovsky, Léon Bottou
Abstract	The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. In order to substantiate our theoretical analysis, we perform targeted experiments to verify our assumptions, illustrate our claims, and quantify the phenomena. This paper is divided into three sections. The first section introduces the problem at hand. The second section is dedicated to studying and proving rigorously the problems including instability and saturation that arize when training generative adversarial networks. The third section examines a practical and theoretically grounded direction towards solving these problems, while introducing new tools to study them.
Tasks
Published	2017-01-17
URL	http://arxiv.org/abs/1701.04862v1
PDF	http://arxiv.org/pdf/1701.04862v1.pdf
PWC	https://paperswithcode.com/paper/towards-principled-methods-for-training
Repo	https://github.com/voqtuyen/GAN-Intuition
Framework	none

Conic Scan-and-Cover algorithms for nonparametric topic modeling


Title	Conic Scan-and-Cover algorithms for nonparametric topic modeling
Authors	Mikhail Yurochkin, Aritra Guha, XuanLong Nguyen
Abstract	We propose new algorithms for topic modeling when the number of topics is unknown. Our approach relies on an analysis of the concentration of mass and angular geometry of the topic simplex, a convex polytope constructed by taking the convex hull of vertices representing the latent topics. Our algorithms are shown in practice to have accuracy comparable to a Gibbs sampler in terms of topic estimation, which requires the number of topics be given. Moreover, they are one of the fastest among several state of the art parametric techniques. Statistical consistency of our estimator is established under some conditions.
Tasks
Published	2017-10-09
URL	http://arxiv.org/abs/1710.02952v1
PDF	http://arxiv.org/pdf/1710.02952v1.pdf
PWC	https://paperswithcode.com/paper/conic-scan-and-cover-algorithms-for
Repo	https://github.com/moonfolk/Geometric-Topic-Modeling
Framework	none

Angle-Based Joint and Individual Variation Explained


Title	Angle-Based Joint and Individual Variation Explained
Authors	Qing Feng, Meilei Jiang, Jan Hannig, J. S. Marron
Abstract	Integrative analysis of disparate data blocks measured on a common set of experimental subjects is a major challenge in modern data analysis. This data structure naturally motivates the simultaneous exploration of the joint and individual variation within each data block resulting in new insights. For instance, there is a strong desire to integrate the multiple genomic data sets in The Cancer Genome Atlas to characterize the common and also the unique aspects of cancer genetics and cell biology for each source. In this paper we introduce Angle-Based Joint and Individual Variation Explained capturing both joint and individual variation within each data block. This is a major improvement over earlier approaches to this challenge in terms of a new conceptual understanding, much better adaption to data heterogeneity and a fast linear algebra computation. Important mathematical contributions are the use of score subspaces as the principal descriptors of variation structure and the use of perturbation theory as the guide for variation segmentation. This leads to an exploratory data analysis method which is insensitive to the heterogeneity among data blocks and does not require separate normalization. An application to cancer data reveals different behaviors of each type of signal in characterizing tumor subtypes. An application to a mortality data set reveals interesting historical lessons. Software and data are available at GitHub https://github.com/MeileiJiang/AJIVE_Project.
Tasks
Published	2017-04-07
URL	http://arxiv.org/abs/1704.02060v3
PDF	http://arxiv.org/pdf/1704.02060v3.pdf
PWC	https://paperswithcode.com/paper/angle-based-joint-and-individual-variation
Repo	https://github.com/MeileiJiang/AJIVE_Project
Framework	none

Using Titles vs. Full-text as Source for Automated Semantic Document Annotation


Title	Using Titles vs. Full-text as Source for Automated Semantic Document Annotation
Authors	Lukas Galke, Florian Mai, Alan Schelten, Dennis Brunsch, Ansgar Scherp
Abstract	A significant part of the largest Knowledge Graph today, the Linked Open Data cloud, consists of metadata about documents such as publications, news reports, and other media articles. While the widespread access to the document metadata is a tremendous advancement, it is yet not so easy to assign semantic annotations and organize the documents along semantic concepts. Providing semantic annotations like concepts in SKOS thesauri is a classical research topic, but typically it is conducted on the full-text of the documents. For the first time, we offer a systematic comparison of classification approaches to investigate how far semantic annotations can be conducted using just the metadata of the documents such as titles published as labels on the Linked Open Data cloud. We compare the classifications obtained from analyzing the documents’ titles with semantic annotations obtained from analyzing the full-text. Apart from the prominent text classification baselines kNN and SVM, we also compare recent techniques of Learning to Rank and neural networks and revisit the traditional methods logistic regression, Rocchio, and Naive Bayes. The results show that across three of our four datasets, the performance of the classifications using only titles reaches over 90% of the quality compared to the classification performance when using the full-text. Thus, conducting document classification by just using the titles is a reasonable approach for automated semantic annotation and opens up new possibilities for enriching Knowledge Graphs.
Tasks	Document Classification, Knowledge Graphs, Learning-To-Rank, Text Classification
Published	2017-05-15
URL	http://arxiv.org/abs/1705.05311v2
PDF	http://arxiv.org/pdf/1705.05311v2.pdf
PWC	https://paperswithcode.com/paper/using-titles-vs-full-text-as-source-for
Repo	https://github.com/Quadflor/quadflor
Framework	tf

Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification


Title	Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification
Authors	Hong-Xing Yu, Ancong Wu, Wei-Shi Zheng
Abstract	While metric learning is important for Person re-identification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount of data over multiple disjoint camera views is available but not labelled. To overcome the problem, we propose unsupervised asymmetric metric learning for unsupervised RE-ID. Our model aims to learn an asymmetric metric, i.e., specific projection for each view, based on asymmetric clustering on cross-view person images. Our model finds a shared space where view-specific bias is alleviated and thus better matching performance can be achieved. Extensive experiments have been conducted on a baseline and five large-scale RE-ID datasets to demonstrate the effectiveness of the proposed model. Through the comparison, we show that our model works much more suitable for unsupervised RE-ID compared to classical unsupervised metric learning models. We also compare with existing unsupervised RE-ID methods, and our model outperforms them with notable margins. Specifically, we report the results on large-scale unlabelled RE-ID dataset, which is important but unfortunately less concerned in literatures.
Tasks	Metric Learning, Person Re-Identification, Unsupervised Person Re-Identification
Published	2017-08-27
URL	http://arxiv.org/abs/1708.08062v2
PDF	http://arxiv.org/pdf/1708.08062v2.pdf
PWC	https://paperswithcode.com/paper/cross-view-asymmetric-metric-learning-for
Repo	https://github.com/KovenYu/CAMEL
Framework	none

Depression Scale Recognition from Audio, Visual and Text Analysis


Title	Depression Scale Recognition from Audio, Visual and Text Analysis
Authors	Shubham Dham, Anirudh Sharma, Abhinav Dhall
Abstract	Depression is a major mental health disorder that is rapidly affecting lives worldwide. Depression not only impacts emotional but also physical and psychological state of the person. Its symptoms include lack of interest in daily activities, feeling low, anxiety, frustration, loss of weight and even feeling of self-hatred. This report describes work done by us for Audio Visual Emotion Challenge (AVEC) 2017 during our second year BTech summer internship. With the increase in demand to detect depression automatically with the help of machine learning algorithms, we present our multimodal feature extraction and decision level fusion approach for the same. Features are extracted by processing on the provided Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector approach were applied on the visual data; statistical descriptors on gaze, pose; low level audio features and head pose and text features were also extracted. Classification is done on fused as well as independent features using Support Vector Machine (SVM) and neural networks. The results obtained were able to cross the provided baseline on validation data set by 17% on audio features and 24.5% on video features.
Tasks
Published	2017-09-18
URL	http://arxiv.org/abs/1709.05865v1
PDF	http://arxiv.org/pdf/1709.05865v1.pdf
PWC	https://paperswithcode.com/paper/depression-scale-recognition-from-audio
Repo	https://github.com/yijiazh/DFER_Summer2019
Framework	tf

Quasar: Datasets for Question Answering by Search and Reading


Title	Quasar: Datasets for Question Answering by Search and Reading
Authors	Bhuwan Dhingra, Kathryn Mazaitis, William W. Cohen
Abstract	We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. The Quasar-S dataset consists of 37000 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website Stack Overflow. The posts and comments on the website serve as the background corpus for answering the cloze questions. The Quasar-T dataset consists of 43000 open-domain trivia questions and their answers obtained from various internet sources. ClueWeb09 serves as the background corpus for extracting these answers. We pose these datasets as a challenge for two related subtasks of factoid Question Answering: (1) searching for relevant pieces of text that include the correct answer to a query, and (2) reading the retrieved text to answer the query. We also describe a retrieval system for extracting relevant sentences and documents from the corpus given a query, and include these in the release for researchers wishing to only focus on (2). We evaluate several baselines on both datasets, ranging from simple heuristics to powerful neural models, and show that these lag behind human performance by 16.4% and 32.1% for Quasar-S and -T respectively. The datasets are available at https://github.com/bdhingra/quasar .
Tasks	Question Answering
Published	2017-07-12
URL	http://arxiv.org/abs/1707.03904v2
PDF	http://arxiv.org/pdf/1707.03904v2.pdf
PWC	https://paperswithcode.com/paper/quasar-datasets-for-question-answering-by
Repo	https://github.com/bdhingra/quasar
Framework	none

Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features


Title	Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features
Authors	Fan Yang, Arjun Mukherjee, Eduard Dragut
Abstract	Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore believe the news as true news. We observe that satirical cues are often reflected in certain paragraphs rather than the whole document. Existing works only consider document-level features to detect the satire, which could be limited. We consider paragraph-level linguistic features to unveil the satire by incorporating neural network and attention mechanism. We investigate the difference between paragraph-level features and document-level features, and analyze them on a large satirical news dataset. The evaluation shows that the proposed model detects satirical news effectively and reveals what features are important at which level.
Tasks
Published	2017-09-04
URL	http://arxiv.org/abs/1709.01189v1
PDF	http://arxiv.org/pdf/1709.01189v1.pdf
PWC	https://paperswithcode.com/paper/satirical-news-detection-and-analysis-using
Repo	https://github.com/fYYw/satire
Framework	none

Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis


Title	Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis
Authors	Eduardo Valle, Michel Fornaciali, Afonso Menegola, Julia Tavares, Flávia Vasques Bittencourt, Lin Tzy Li, Sandra Avila
Abstract	Deep learning fostered a leap ahead in automated skin lesion analysis in the last two years. Those models are expensive to train and difficult to parameterize. Objective: We investigate methodological issues for designing and evaluating deep learning models for skin lesion analysis. We explore 10 choices faced by researchers: use of transfer learning, model architecture, train dataset, image resolution, type of data augmentation, input normalization, use of segmentation, duration of training, additional use of SVMs, and test data augmentation. Methods: We perform two full factorial experiments, for five different test datasets, resulting in 2560 exhaustive trials in our main experiment, and 1280 trials in our assessment of transfer learning. We analyze both with multi-way ANOVA. We use the exhaustive trials to simulate sequential decisions and ensembles, with and without the use of privileged information from the test set. Results – main experiment: Amount of train data has disproportionate influence, explaining almost half the variation in performance. Of the other factors, test data augmentation and input resolution are the most influential. Deeper models, when combined, with extra data, also help. – transfer experiment: Transfer learning is critical, its absence brings huge performance penalties. – simulations: Ensembles of models are the best option to provide reliable results with limited resources, without using privileged information and sacrificing methodological rigor. Conclusions and Significance: Advancing research on automated skin lesion analysis requires curating larger public datasets. Indirect use of privileged information from the test set to design the models is a subtle, but frequent methodological mistake that leads to overoptimistic results. Ensembles of models are a cost-effective alternative to the expensive full-factorial and to the unstable sequential designs.
Tasks	Data Augmentation, Transfer Learning
Published	2017-11-01
URL	https://arxiv.org/abs/1711.00441v4
PDF	https://arxiv.org/pdf/1711.00441v4.pdf
PWC	https://paperswithcode.com/paper/data-depth-and-design-learning-reliable
Repo	https://github.com/learningtitans/data-depth-design
Framework	tf

Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis


Title	Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis
Authors	Mohammad Malekzadeh, Richard G. Clegg, Hamed Haddadi
Abstract	An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially sensitive information about the individuals can also be discovered through access to sensory data and this cannot easily be protected using traditional privacy approaches. In this paper, we propose a privacy-preserving sensing framework for managing access to time-series data in order to provide utility while protecting individuals’ privacy. We introduce Replacement AutoEncoder, a novel algorithm which learns how to transform discriminative features of data that correspond to sensitive inferences, into some features that have been more observed in non-sensitive inferences, to protect users’ privacy. This efficiency is achieved by defining a user-customized objective function for deep autoencoders. Our replacement method will not only eliminate the possibility of recognizing sensitive inferences, it also eliminates the possibility of detecting the occurrence of them. That is the main weakness of other approaches such as filtering or randomization. We evaluate the efficacy of the algorithm with an activity recognition task in a multi-sensing environment using extensive experiments on three benchmark datasets. We show that it can retain the recognition accuracy of state-of-the-art techniques while simultaneously preserving the privacy of sensitive information. Finally, we utilize the GANs for detecting the occurrence of replacement, after releasing data, and show that this can be done only if the adversarial network is trained on the users’ original data.
Tasks	Activity Recognition, Time Series
Published	2017-10-18
URL	http://arxiv.org/abs/1710.06564v3
PDF	http://arxiv.org/pdf/1710.06564v3.pdf
PWC	https://paperswithcode.com/paper/replacement-autoencoder-a-privacy-preserving
Repo	https://github.com/mmalekzadeh/replacement-autoencoder
Framework	tf

Self-Attentive Residual Decoder for Neural Machine Translation


Title	Self-Attentive Residual Decoder for Neural Machine Translation
Authors	Lesly Miculicich Werlen, Nikolaos Pappas, Dhananjay Ram, Andrei Popescu-Belis
Abstract	Neural sequence-to-sequence networks with attention have achieved remarkable performance for machine translation. One of the reasons for their effectiveness is their ability to capture relevant source-side contextual information at each time-step prediction through an attention mechanism. However, the target-side context is solely based on the sequence model which, in practice, is prone to a recency bias and lacks the ability to capture effectively non-sequential dependencies among words. To address this limitation, we propose a target-side-attentive residual recurrent network for decoding, where attention over previous words contributes directly to the prediction of the next word. The residual learning facilitates the flow of information from the distant past and is able to emphasize any of the previously translated words, hence it gains access to a wider context. The proposed model outperforms a neural MT baseline as well as a memory and self-attention network on three language pairs. The analysis of the attention learned by the decoder confirms that it emphasizes a wider context, and that it captures syntactic-like structures.
Tasks	Machine Translation
Published	2017-09-14
URL	http://arxiv.org/abs/1709.04849v5
PDF	http://arxiv.org/pdf/1709.04849v5.pdf
PWC	https://paperswithcode.com/paper/self-attentive-residual-decoder-for-neural
Repo	https://github.com/idiap/Attentive_Residual_Connections_NMT
Framework	none