January 27, 2020

3052 words 15 mins read

Paper Group ANR 1276

Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events. Graph-based Multi-view Binary Learning for Image Clustering. Toward a Computational Theory of Evidence-Based Reasoning for Instructable Cognitive Agents. Learning eating environments through scene clustering. Online Debiasing for Adaptively Coll …

Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events


Title	Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events
Authors	Sharaj Panwar, Paul Rad, Tzyy-Ping Jung, Yufei Huang
Abstract	Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful deep learning model with the limited EEG data. Being able to generate EEG data computationally could address this limitation. We propose a novel Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to synthesize EEG data. This network addresses several modeling challenges of simulating time-series EEG data including frequency artifacts and training instability. We further extended this network to a class-conditioned variant that also includes a classification branch to perform event-related classification. We trained the proposed networks to generate one and 64-channel data resembling EEG signals routinely seen in a rapid serial visual presentation (RSVP) experiment and demonstrated the validity of the generated samples. We also tested intra-subject cross-session classification performance for classifying the RSVP target events and showed that class-conditioned WGAN-GP can achieve improved event-classification performance over EEGNet.
Tasks	EEG, Time Series
Published	2019-11-11
URL	https://arxiv.org/abs/1911.04379v1
PDF	https://arxiv.org/pdf/1911.04379v1.pdf
PWC	https://paperswithcode.com/paper/modeling-eeg-data-distribution-with-a
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Graph-based Multi-view Binary Learning for Image Clustering


Title	Graph-based Multi-view Binary Learning for Image Clustering
Authors	Guangqi Jiang, Huibing Wang, Jinjia Peng, Dongyan Chen, Xianping Fu
Abstract	Hashing techniques, also known as binary code learning, have recently gained increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering methods are single-view ones, which lack complete structure or complementary information from multiple views. For cluster tasks, abundant prior researches mainly focus on learning discrete hash code while few works take original data structure into consideration. To address these problems, we propose a novel binary code algorithm for clustering, which adopts graph embedding to preserve the original data structure, called (Graph-based Multi-view Binary Learning) GMBL in this paper. GMBL mainly focuses on encoding the information of multiple views into a compact binary code, which explores complementary information from multiple views. In particular, in order to maintain the graph-based structure of the original data, we adopt a Laplacian matrix to preserve the local linear relationship of the data and map it to the Hamming space. Considering different views have distinctive contributions to the final clustering results, GMBL adopts a strategy of automatically assign weights for each view to better guide the clustering. Finally, An alternating iterative optimization method is adopted to optimize discrete binary codes directly instead of relaxing the binary constraint in two steps. Experiments on five public datasets demonstrate the superiority of our proposed method compared with previous approaches in terms of clustering performance.
Tasks	Graph Embedding, Image Clustering
Published	2019-12-11
URL	https://arxiv.org/abs/1912.05159v1
PDF	https://arxiv.org/pdf/1912.05159v1.pdf
PWC	https://paperswithcode.com/paper/graph-based-multi-view-binary-learning-for
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Toward a Computational Theory of Evidence-Based Reasoning for Instructable Cognitive Agents


Title	Toward a Computational Theory of Evidence-Based Reasoning for Instructable Cognitive Agents
Authors	Gheorghe Tecuci, Dorin Marcu, Mihai Boicu, Steven Meckl, Chirag Uttamsingh
Abstract	Evidence-based reasoning is at the core of many problem-solving and decision-making tasks in a wide variety of domains. Generalizing from the research and development of cognitive agents in several such domains, this paper presents progress toward a computational theory for the development of instructable cognitive agents for evidence-based reasoning tasks. The paper also illustrates the application of this theory to the development of four prototype cognitive agents in domains that are critical to the government and the public sector. Two agents function as cognitive assistants, one in intelligence analysis, and the other in science education. The other two agents operate autonomously, one in cybersecurity and the other in intelligence, surveillance, and reconnaissance. The paper concludes with the directions of future research on the proposed computational theory.
Tasks	Decision Making
Published	2019-10-09
URL	https://arxiv.org/abs/1910.03990v1
PDF	https://arxiv.org/pdf/1910.03990v1.pdf
PWC	https://paperswithcode.com/paper/toward-a-computational-theory-of-evidence
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Learning eating environments through scene clustering


Title	Learning eating environments through scene clustering
Authors	Sri Kalyan Yarlagadda, Sriram Baireddy, David Güera, Carol J. Boushey, Deborah A. Kerr, Fengqing Zhu
Abstract	It is well known that dietary habits have a significant influence on health. While many studies have been conducted to understand this relationship, little is known about the relationship between eating environments and health. Yet researchers and health agencies around the world have recognized the eating environment as a promising context for improving diet and health. In this paper, we propose an image clustering method to automatically extract the eating environments from eating occasion images captured during a community dwelling dietary study. Specifically, we are interested in learning how many different environments an individual consumes food in. Our method clusters images by extracting features at both global and local scales using a deep neural network. The variation in the number of clusters and images captured by different individual makes this a very challenging problem. Experimental results show that our method performs significantly better compared to several existing clustering approaches.
Tasks	Image Clustering
Published	2019-10-24
URL	https://arxiv.org/abs/1910.11367v2
PDF	https://arxiv.org/pdf/1910.11367v2.pdf
PWC	https://paperswithcode.com/paper/learning-eating-environments-through-scene
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Online Debiasing for Adaptively Collected High-dimensional Data


Title	Online Debiasing for Adaptively Collected High-dimensional Data
Authors	Yash Deshpande, Adel Javanmard, Mohammad Mehrabi
Abstract	Adaptive collection of data is commonplace in applications throughout science and engineering. From the point of view of statistical inference however, adaptive data collection induces memory and correlation in the sample, and poses significant challenge. We consider the high-dimensional linear regression, where the sample is collected adaptively, and the sample size $n$ can be smaller than $p$, the number of covariates. In this setting, there are two distinct sources of bias: the first due to regularization imposed for consistent estimation, e.g. using the LASSO, and the second due to adaptivity in collecting the sample. We propose \emph{`online debiasing’}, a general procedure for estimators such as the LASSO, which addresses both sources of bias. In two concrete contexts $(i)$ batched data collection and $(ii)$ time series analysis, we demonstrate that online debiasing optimally debiases the LASSO estimate when the underlying parameter $\theta_0$ has sparsity of order $o(\sqrt{n}/\log p)$. In this regime, the debiased estimator can be used to compute $p$-values and confidence intervals of optimal size. \|
Tasks	Time Series, Time Series Analysis
Published	2019-11-04
URL	https://arxiv.org/abs/1911.01040v2
PDF	https://arxiv.org/pdf/1911.01040v2.pdf
PWC	https://paperswithcode.com/paper/online-debiasing-for-adaptively-collected
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Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering


Title	Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
Authors	Li Zhou, Kevin Small
Abstract	Multi-domain dialogue state tracking (DST) is a critical component for conversational AI systems. The domain ontology (i.e., specification of domains, slots, and values) of a conversational AI system is generally incomplete, making the capability for DST models to generalize to new slots, values, and domains during inference imperative. In this paper, we propose to model multi-domain DST as a question answering problem, referred to as Dialogue State Tracking via Question Answering (DSTQA). Within DSTQA, each turn generates a question asking for the value of a (domain, slot) pair, thus making it naturally extensible to unseen domains, slots, and values. Additionally, we use a dynamically-evolving knowledge graph to explicitly learn relationships between (domain, slot) pairs. Our model has a 5.80% and 12.21% relative improvement over the current state-of-the-art model on MultiWOZ 2.0 and MultiWOZ 2.1 datasets, respectively. Additionally, our model consistently outperforms the state-of-the-art model in domain adaptation settings.
Tasks	Dialogue State Tracking, Domain Adaptation, Question Answering
Published	2019-11-07
URL	https://arxiv.org/abs/1911.06192v1
PDF	https://arxiv.org/pdf/1911.06192v1.pdf
PWC	https://paperswithcode.com/paper/multi-domain-dialogue-state-tracking-as
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Reconstruction of Privacy-Sensitive Data from Protected Templates


Title	Reconstruction of Privacy-Sensitive Data from Protected Templates
Authors	Shideh Rezaeifar, Behrooz Razeghi, Olga Taran, Taras Holotyak, Slava Voloshynovskiy
Abstract	In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA). The STCA is a generalization of randomization techniques which includes random projections, lossy quantization, and addition of ambiguization noise to satisfy the privacy-utility trade-off requirements. The theoretical privacy-preserving properties of STCA have been validated on synthetic data. However, the applicability of STCA to real data and potential threats linked to reconstruction based on recent deep reconstruction algorithms are still open problems. Our results demonstrate that STCA still achieves the claimed theoretical performance when facing deep reconstruction attacks for the synthetic i.i.d. data, while for real images special measures are required to guarantee proper protection of the templates.
Tasks	Quantization
Published	2019-05-08
URL	https://arxiv.org/abs/1905.03282v1
PDF	https://arxiv.org/pdf/1905.03282v1.pdf
PWC	https://paperswithcode.com/paper/190503282
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Optimal Transport Based Change Point Detection and Time Series Segment Clustering


Title	Optimal Transport Based Change Point Detection and Time Series Segment Clustering
Authors	Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Erika Hussey, Eric L. Miller
Abstract	Two common problems in time series analysis are the decomposition of the data stream into disjoint segments that are each in some sense “homogeneous” - a problem known as Change Point Detection (CPD) - and the grouping of similar nonadjacent segments, a problem that we call Time Series Segment Clustering (TSSC). Building upon recent theoretical advances characterizing the limiting distribution-free behavior of the Wasserstein two-sample test (Ramdas et al. 2015), we propose a novel algorithm for unsupervised, distribution-free CPD which is amenable to both offline and online settings. We also introduce a method to mitigate false positives in CPD and address TSSC by using the Wasserstein distance between the detected segments to build an affinity matrix to which we apply spectral clustering. Results on both synthetic and real data sets show the benefits of the approach.
Tasks	Change Point Detection, Time Series, Time Series Analysis
Published	2019-11-04
URL	https://arxiv.org/abs/1911.01325v2
PDF	https://arxiv.org/pdf/1911.01325v2.pdf
PWC	https://paperswithcode.com/paper/optimal-transport-based-change-point
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Accounting for Physics Uncertainty in Ultrasonic Wave Propagation using Deep Learning


Title	Accounting for Physics Uncertainty in Ultrasonic Wave Propagation using Deep Learning
Authors	Ishan D. Khurjekar, Joel B. Harley
Abstract	Ultrasonic guided waves are commonly used to localize structural damage in infrastructures such as buildings, airplanes, bridges. Damage localization can be viewed as an inverse problem. Physical model based techniques are popular for guided wave based damage localization. The performance of these techniques depend on the degree of faithfulness with which the physical model describes wave propagation. External factors such as environmental variations and random noise are a source of uncertainty in wave propagation. The physical modeling of uncertainty in an inverse problem is still a challenging problem. In this work, we propose a deep learning based model for robust damage localization in presence of uncertainty. Wave data with uncertainty is simulated to reflect variations due to external factors and Gaussian noise is added to reflect random noise in the environment. After evaluating the localization error on test data with uncertainty, we observe that the deep learning model trained with uncertainty can learn robust representations. The approach shows potential for dealing with uncertainty in physical science problems using deep learning models.
Tasks
Published	2019-11-07
URL	https://arxiv.org/abs/1911.02743v1
PDF	https://arxiv.org/pdf/1911.02743v1.pdf
PWC	https://paperswithcode.com/paper/accounting-for-physics-uncertainty-in
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Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community


Title	Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community
Authors	Kumar Bhargav Srinivasan, Cristian Danescu-Niculescu-Mizil, Lillian Lee, Chenhao Tan
Abstract	Moderators of online communities often employ comment deletion as a tool. We ask here whether, beyond the positive effects of shielding a community from undesirable content, does comment removal actually cause the behavior of the comment’s author to improve? We examine this question in a particularly well-moderated community, the ChangeMyView subreddit. The standard analytic approach of interrupted time-series analysis unfortunately cannot answer this question of causality because it fails to distinguish the effect of having made a non-compliant comment from the effect of being subjected to moderator removal of that comment. We therefore leverage a “delayed feedback” approach based on the observation that some users may remain active between the time when they posted the non-compliant comment and the time when that comment is deleted. Applying this approach to such users, we reveal the causal role of comment deletion in reducing immediate noncompliance rates, although we do not find evidence of it having a causal role in inducing other behavior improvements. Our work thus empirically demonstrates both the promise and some potential limits of content removal as a positive moderation strategy, and points to future directions for identifying causal effects from observational data.
Tasks	Time Series, Time Series Analysis
Published	2019-10-21
URL	https://arxiv.org/abs/1910.09563v1
PDF	https://arxiv.org/pdf/1910.09563v1.pdf
PWC	https://paperswithcode.com/paper/content-removal-as-a-moderation-strategy
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Adaptive Weighting Depth-variant Deconvolution of Fluorescence Microscopy Images with Convolutional Neural Network


Title	Adaptive Weighting Depth-variant Deconvolution of Fluorescence Microscopy Images with Convolutional Neural Network
Authors	Da He, De Cai, Jiasheng Zhou, Jiajia Luo, Sung-Liang Chen
Abstract	Fluorescence microscopy plays an important role in biomedical research. The depth-variant point spread function (PSF) of a fluorescence microscope produces low-quality images especially in the out-of-focus regions of thick specimens. Traditional deconvolution to restore the out-of-focus images is usually insufficient since a depth-invariant PSF is assumed. This article aims at handling fluorescence microscopy images by learning-based depth-variant PSF and reducing artifacts. We propose adaptive weighting depth-variant deconvolution (AWDVD) with defocus level prediction convolutional neural network (DelpNet) to restore the out-of-focus images. Depth-variant PSFs of image patches can be obtained by DelpNet and applied in the afterward deconvolution. AWDVD is adopted for a whole image which is patch-wise deconvolved and appropriately cropped before deconvolution. DelpNet achieves the accuracy of 98.2%, which outperforms the best-ever one using the same microscopy dataset. Image patches of 11 defocus levels after deconvolution are validated with maximum improvement in the peak signal-to-noise ratio and structural similarity index of 6.6 dB and 11%, respectively. The adaptive weighting of the patch-wise deconvolved image can eliminate patch boundary artifacts and improve deconvolved image quality. The proposed method can accurately estimate depth-variant PSF and effectively recover out-of-focus microscopy images. To our acknowledge, this is the first study of handling out-of-focus microscopy images using learning-based depth-variant PSF. Facing one of the most common blurs in fluorescence microscopy, the novel method provides a practical technology to improve the image quality.
Tasks
Published	2019-07-07
URL	https://arxiv.org/abs/1907.03217v1
PDF	https://arxiv.org/pdf/1907.03217v1.pdf
PWC	https://paperswithcode.com/paper/adaptive-weighting-depth-variant
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Galaxy Image Simulation Using Progressive GANs


Title	Galaxy Image Simulation Using Progressive GANs
Authors	Mohamad Dia, Elodie Savary, Martin Melchior, Frederic Courbin
Abstract	In this work, we provide an efficient and realistic data-driven approach to simulate astronomical images using deep generative models from machine learning. Our solution is based on a variant of the generative adversarial network (GAN) with progressive training methodology and Wasserstein cost function. The proposed solution generates naturalistic images of galaxies that show complex structures and high diversity, which suggests that data-driven simulations using machine learning can replace many of the expensive model-driven methods used in astronomical data processing.
Tasks
Published	2019-09-26
URL	https://arxiv.org/abs/1909.12160v1
PDF	https://arxiv.org/pdf/1909.12160v1.pdf
PWC	https://paperswithcode.com/paper/galaxy-image-simulation-using-progressive
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Framework

Measuring Conversational Fluidity in Automated Dialogue Agents


Title	Measuring Conversational Fluidity in Automated Dialogue Agents
Authors	Keith Vella, Massimo Poesio, Michael Sigamani, Cihan Dogan, Aimore Dutra, Dimitrios Dimakopoulos, Alfredo Gemma, Ella Walters
Abstract	We present an automated evaluation method to measure fluidity in conversational dialogue systems. The method combines various state of the art Natural Language tools into a classifier, and human ratings on these dialogues to train an automated judgment model. Our experiments show that the results are an improvement on existing metrics for measuring fluidity.
Tasks
Published	2019-10-25
URL	https://arxiv.org/abs/1910.11790v1
PDF	https://arxiv.org/pdf/1910.11790v1.pdf
PWC	https://paperswithcode.com/paper/measuring-conversational-fluidity-in
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Few-Shot Abstract Visual Reasoning With Spectral Features


Title	Few-Shot Abstract Visual Reasoning With Spectral Features
Authors	Tanner Bohn, Yining Hu, Charles X. Ling
Abstract	We present an image preprocessing technique capable of improving the performance of few-shot classifiers on abstract visual reasoning tasks. Many visual reasoning tasks with abstract features are easy for humans to learn with few examples but very difficult for computer vision approaches with the same number of samples, despite the ability for deep learning models to learn abstract features. Same-different (SD) problems represent a type of visual reasoning task requiring knowledge of pattern repetition within individual images, and modern computer vision approaches have largely faltered on these classification problems, even when provided with vast amounts of training data. We propose a simple method for solving these problems based on the insight that removing peaks from the amplitude spectrum of an image is capable of emphasizing the unique parts of the image. When combined with several classifiers, our method performs well on the SD SVRT tasks with few-shot learning, improving upon the best comparable results on all tasks, with average absolute accuracy increases nearly 40% for some classifiers. In particular, we find that combining Relational Networks with this image preprocessing approach improves their performance from chance-level to over 90% accuracy on several SD tasks.
Tasks	Few-Shot Learning, Visual Reasoning
Published	2019-10-04
URL	https://arxiv.org/abs/1910.01833v1
PDF	https://arxiv.org/pdf/1910.01833v1.pdf
PWC	https://paperswithcode.com/paper/few-shot-abstract-visual-reasoning-with
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A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network


Title	A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network
Authors	Shin Kamada, Takumi Ichimura
Abstract	Deep learning builds deep architectures such as multi-layered artificial neural networks to effectively represent multiple features of input patterns. The adaptive structural learning method of Deep Belief Network (DBN) can realize a high classification capability while searching the optimal network structure during the training. The method can find the optimal number of hidden neurons of a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm to train the given input data, and then it can make a new layer in DBN by the layer generation algorithm to actualize a deep data representation. Moreover, the learning algorithm of Adaptive RBM and Adaptive DBN was extended to the time-series analysis by using the idea of LSTM (Long Short Term Memory). In this paper, our proposed prediction method was applied to Moving MNIST, which is a benchmark data set for video recognition. We challenge to reveal the power of our proposed method in the video recognition research field, since video includes rich source of visual information. Compared with the LSTM model, our method showed higher prediction performance (more than 90% predication accuracy for test data).
Tasks	Time Series, Time Series Analysis, Video Recognition
Published	2019-09-30
URL	https://arxiv.org/abs/1909.13480v1
PDF	https://arxiv.org/pdf/1909.13480v1.pdf
PWC	https://paperswithcode.com/paper/a-video-recognition-method-by-using-adaptive
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