January 31, 2020

3176 words 15 mins read

Paper Group ANR 73

Automatic Differentiation for Complex Valued SVD. Big Data Analytics and AI in Mental Healthcare. An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing. Visual Query Answering by Entity-Attribute Graph Matching and Reasoning. Drug-drug interaction prediction based on co-medication patterns and graph matching. Computi …

Automatic Differentiation for Complex Valued SVD


Title	Automatic Differentiation for Complex Valued SVD
Authors	Zhou-Quan Wan, Shi-Xin Zhang
Abstract	In this note, we report the back propagation formula for complex valued singular value decompositions (SVD). This formula is an important ingredient for a complete automatic differentiation(AD) infrastructure in terms of complex numbers, and it is also the key to understand and utilize AD in tensor networks.
Tasks	Tensor Networks
Published	2019-09-04
URL	https://arxiv.org/abs/1909.02659v3
PDF	https://arxiv.org/pdf/1909.02659v3.pdf
PWC	https://paperswithcode.com/paper/automatic-differentiation-for-complex-valued
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Big Data Analytics and AI in Mental Healthcare


Title	Big Data Analytics and AI in Mental Healthcare
Authors	Ariel Rosenfeld, David Benrimoh, Caitrin Armstrong, Nykan Mirchi, Timothe Langlois-Therrien, Colleen Rollins, Myriam Tanguay-Sela, Joseph Mehltretter, Robert Fratila, Sonia Israel, Emily Snook, Kelly Perlman, Akiva Kleinerman, Bechara Saab, Mark Thoburn, Cheryl Gabbay, Amit Yaniv-Rosenfeld
Abstract	Mental health conditions cause a great deal of distress or impairment; depression alone will affect 11% of the world’s population. The application of Artificial Intelligence (AI) and big-data technologies to mental health has great potential for personalizing treatment selection, prognosticating, monitoring for relapse, detecting and helping to prevent mental health conditions before they reach clinical-level symptomatology, and even delivering some treatments. However, unlike similar applications in other fields of medicine, there are several unique challenges in mental health applications which currently pose barriers towards the implementation of these technologies. Specifically, there are very few widely used or validated biomarkers in mental health, leading to a heavy reliance on patient and clinician derived questionnaire data as well as interpretation of new signals such as digital phenotyping. In addition, diagnosis also lacks the same objective ‘gold standard’ as in other conditions such as oncology, where clinicians and researchers can often rely on pathological analysis for confirmation of diagnosis. In this chapter we discuss the major opportunities, limitations and techniques used for improving mental healthcare through AI and big-data. We explore both the computational, clinical and ethical considerations and best practices as well as lay out the major researcher directions for the near future.
Tasks
Published	2019-03-12
URL	http://arxiv.org/abs/1903.12071v1
PDF	http://arxiv.org/pdf/1903.12071v1.pdf
PWC	https://paperswithcode.com/paper/big-data-analytics-and-ai-in-mental
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An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing


Title	An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing
Authors	Hashem Ezzati, Mahmood Amintoosi, Hashem Tabasi
Abstract	Graph matching is one of the most important problems in graph theory and combinatorial optimization, with many applications in various domains. Although meta-heuristic algorithms have had good performance on many NP-Hard and NP-Complete problems, for this problem there are not reported superior solutions by these algorithms. The reason of this inefficiency has not been investigated yet. In this paper we show that simulated annealing as an stochastic optimization method is unlikely to be even close to the optimal solution for this problem. In addition to theoretical discussion, the experimental results also verified our idea; for example, in two sample graphs, the probability of reaching to a solution with more than three correct matches is about $0.02$ in simulated annealing.
Tasks	Combinatorial Optimization, Graph Matching, Stochastic Optimization
Published	2019-03-29
URL	http://arxiv.org/abs/1903.12527v1
PDF	http://arxiv.org/pdf/1903.12527v1.pdf
PWC	https://paperswithcode.com/paper/an-upper-bound-for-minimum-true-matches-in
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Visual Query Answering by Entity-Attribute Graph Matching and Reasoning


Title	Visual Query Answering by Entity-Attribute Graph Matching and Reasoning
Authors	Peixi Xiong, Huayi Zhan, Xin Wang, Baivab Sinha, Ying Wu
Abstract	Visual Query Answering (VQA) is of great significance in offering people convenience: one can raise a question for details of objects, or high-level understanding about the scene, over an image. This paper proposes a novel method to address the VQA problem. In contrast to prior works, our method that targets single scene VQA, replies on graph-based techniques and involves reasoning. In a nutshell, our approach is centered on three graphs. The first graph, referred to as inference graph GI , is constructed via learning over labeled data. The other two graphs, referred to as query graph Q and entity-attribute graph GEA, are generated from natural language query Qnl and image Img, that are issued from users, respectively. As GEA often does not take sufficient information to answer Q, we develop techniques to infer missing information of GEA with GI . Based on GEA and Q, we provide techniques to find matches of Q in GEA, as the answer of Qnl in Img. Unlike commonly used VQA methods that are based on end-to-end neural networks, our graph-based method shows well-designed reasoning capability, and thus is highly interpretable. We also create a dataset on soccer match (Soccer-VQA) with rich annotations. The experimental results show that our approach outperforms the state-of-the-art method and has high potential for future investigation.
Tasks	Graph Matching, Visual Question Answering
Published	2019-03-16
URL	http://arxiv.org/abs/1903.06994v1
PDF	http://arxiv.org/pdf/1903.06994v1.pdf
PWC	https://paperswithcode.com/paper/visual-query-answering-by-entity-attribute
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Drug-drug interaction prediction based on co-medication patterns and graph matching


Title	Drug-drug interaction prediction based on co-medication patterns and graph matching
Authors	Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning
Abstract	Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript. Methods: Novel kernels over drug combinations of arbitrary orders are developed within support vector machines for the prediction. Graph matching methods are used in the novel kernels to measure the similarities among drug combinations, in which drug co-medication patterns are leveraged to measure single drug similarities. Results: The experimental results on a real-world dataset demonstrated that the new kernels achieve an area under the curve (AUC) value 0.912 for the prediction problem. Conclusions: The new methods with drug co-medication based single drug similarities can accurately predict whether a drug combination is likely to induce adverse drug reactions of interest. Keywords: drug-drug interaction prediction; drug combination similarity; co-medication; graph matching
Tasks	Graph Matching
Published	2019-02-22
URL	http://arxiv.org/abs/1902.08675v1
PDF	http://arxiv.org/pdf/1902.08675v1.pdf
PWC	https://paperswithcode.com/paper/drug-drug-interaction-prediction-based-on-co
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Computing Optimal Assignments in Linear Time for Approximate Graph Matching


Title	Computing Optimal Assignments in Linear Time for Approximate Graph Matching
Authors	Nils M. Kriege, Pierre-Louis Giscard, Franka Bause, Richard C. Wilson
Abstract	Finding an optimal assignment between two sets of objects is a fundamental problem arising in many applications, including the matching of `bag-of-words’ representations in natural language processing and computer vision. Solving the assignment problem typically requires cubic time and its pairwise computation is expensive on large datasets. In this paper, we develop an algorithm which can find an optimal assignment in linear time when the cost function between objects is represented by a tree distance. We employ the method to approximate the edit distance between two graphs by matching their vertices in linear time. To this end, we propose two tree distances, the first of which reflects discrete and structural differences between vertices, and the second of which can be used to compare continuous labels. We verify the effectiveness and efficiency of our methods using synthetic and real-world datasets. \|
Tasks	Graph Matching
Published	2019-01-29
URL	https://arxiv.org/abs/1901.10356v2
PDF	https://arxiv.org/pdf/1901.10356v2.pdf
PWC	https://paperswithcode.com/paper/computing-optimal-assignments-in-linear-time
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Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning


Title	Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning
Authors	Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
Abstract	Large-scale behavioral datasets enable researchers to use complex machine learning algorithms to better predict human behavior, yet this increased predictive power does not always lead to a better understanding of the behavior in question. In this paper, we outline a data-driven, iterative procedure that allows cognitive scientists to use machine learning to generate models that are both interpretable and accurate. We demonstrate this method in the domain of moral decision-making, where standard experimental approaches often identify relevant principles that influence human judgments, but fail to generalize these findings to “real world” situations that place these principles in conflict. The recently released Moral Machine dataset allows us to build a powerful model that can predict the outcomes of these conflicts while remaining simple enough to explain the basis behind human decisions.
Tasks	Decision Making
Published	2019-02-18
URL	https://arxiv.org/abs/1902.06744v3
PDF	https://arxiv.org/pdf/1902.06744v3.pdf
PWC	https://paperswithcode.com/paper/using-machine-learning-to-guide-cognitive
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A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis


Title	A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
Authors	Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Peer-Timo Bremer
Abstract	This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, expressed in terms of the power spectra of the sampling pattern and the function involved. The framework is build in the Euclidean space using Fourier analysis and establishes a connection between some high dimensional geometric objects and optimal spectral form of different state-of-the-art sampling patterns. Subsequently, we estimate the expected error bounds and convergence rate of different state-of-the-art sampling patterns, as the number of samples and dimensions increase. We make several observations about generalization error which are valid irrespective of the approximation scheme (or learning architecture) and training (or optimization) algorithms. Our result also sheds light on ways to formulate design principles for constructing optimal sampling methods for particular problems.
Tasks
Published	2019-06-06
URL	https://arxiv.org/abs/1906.02732v2
PDF	https://arxiv.org/pdf/1906.02732v2.pdf
PWC	https://paperswithcode.com/paper/a-look-at-the-effect-of-sample-design-on
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Local Differential Privacy for Deep Learning


Title	Local Differential Privacy for Deep Learning
Authors	M. A. P. Chamikara, P. Bertok, I. Khalil, D. Liu, S. Camtepe, M. Atiquzzaman
Abstract	The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations such as the amalgamation of software-defined networks (SDN) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data, such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. Existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners’ devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: (1) convolutional module, (2) randomization module, and (3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g. 91%- 96%) with high model quality even under low privacy budgets (e.g. $\varepsilon=0.5$).
Tasks
Published	2019-08-08
URL	https://arxiv.org/abs/1908.02997v3
PDF	https://arxiv.org/pdf/1908.02997v3.pdf
PWC	https://paperswithcode.com/paper/local-differential-privacy-for-deep-learning
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A Deep Learning Framework for Classification of in vitro Multi-Electrode Array Recordings


Title	A Deep Learning Framework for Classification of in vitro Multi-Electrode Array Recordings
Authors	Yun Zhao, Elmer Guzman, Morgane Audouard, Zhuowei Cheng, PaulK. Hansma, Kenneth S. Kosik, Linda Petzold
Abstract	Multi-Electrode Arrays (MEAs) have been widely used to record neuronal activities, which could be used in the diagnosis of gene defects and drug effects. In this paper, we address the problem of classifying in vitro MEA recordings of mouse and human neuronal cultures from different genotypes, where there is no easy way to directly utilize raw sequences as inputs to train an end-to-end classification model. While carefully extracting some features by hand could partially solve the problem, this approach suffers from obvious drawbacks such as difficulty of generalizing. We propose a deep learning framework to address this challenge. Our approach correctly classifies neuronal culture data prepared from two different genotypes – a mouse Knockout of the delta-catenin gene and human induced Pluripotent Stem Cell-derived neurons from Williams syndrome. By splitting the long recordings into short slices for training, and applying Consensus Prediction during testing, our deep learning approach improves the prediction accuracy by 16.69% compared with feature based Logistic Regression for mouse MEA recordings. We further achieve an accuracy of 95.91% using Consensus Prediction in one subset of mouse MEA recording data, which were all recorded at six days in vitro. As high-density MEA recordings become more widely available, this approach could be generalized for classification of neurons carrying different mutations and classification of drug responses.
Tasks
Published	2019-06-05
URL	https://arxiv.org/abs/1906.02241v1
PDF	https://arxiv.org/pdf/1906.02241v1.pdf
PWC	https://paperswithcode.com/paper/a-deep-learning-framework-for-classification
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Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections


Title	Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Authors	Elena Pastorelli, Cristiano Capone, Francesco Simula, Maria V. Sanchez-Vives, Paolo Del Giudice, Maurizio Mattia, Pier Stanislao Paolucci
Abstract	Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz clock rates. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both cortical states in the explored range of inter-modular interconnections.
Tasks
Published	2019-02-22
URL	https://arxiv.org/abs/1902.08410v2
PDF	https://arxiv.org/pdf/1902.08410v2.pdf
PWC	https://paperswithcode.com/paper/scaling-of-a-large-scale-simulation-of
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AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks


Title	AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks
Authors	Jiancheng Lyu, Shuai Zhang, Yingyong Qi, Jack Xin
Abstract	ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its basic operations include group, channel-wise convolution and channel shuffling. However, channel shuffling is manually designed empirically. Mathematically, shuffling is a multiplication by a permutation matrix. In this paper, we propose to automate channel shuffling by learning permutation matrices in network training. We introduce an exact Lipschitz continuous non-convex penalty so that it can be incorporated in the stochastic gradient descent to approximate permutation at high precision. Exact permutations are obtained by simple rounding at the end of training and are used in inference. The resulting network, referred to as AutoShuffleNet, achieved improved classification accuracies on CIFAR-10 and ImageNet data sets. In addition, we found experimentally that the standard convex relaxation of permutation matrices into stochastic matrices leads to poor performance. We prove theoretically the exactness (error bounds) in recovering permutation matrices when our penalty function is zero (very small). We present examples of permutation optimization through graph matching and two-layer neural network models where the loss functions are calculated in closed analytical form. In the examples, convex relaxation failed to capture permutations whereas our penalty succeeded.
Tasks	Graph Matching
Published	2019-01-24
URL	http://arxiv.org/abs/1901.08624v1
PDF	http://arxiv.org/pdf/1901.08624v1.pdf
PWC	https://paperswithcode.com/paper/autoshufflenet-learning-permutation-matrices
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Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos


Title	Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos
Authors	Junpeng Zhang, Xiuping Jia, Jiankun Hu
Abstract	Detecting moving objects from ground-based videos is commonly achieved by using background subtraction techniques. Low-rank matrix decomposition inspires a set of state-of-the-art approaches for this task. It is integrated with structured sparsity regularization to achieve background subtraction in the developed method of Low-rank and Structured Sparse Decomposition (LSD). However, when this method is applied to satellite videos where spatial resolution is poor and targets’ contrast to the background is low, its performance is limited as the data no longer fits adequately either the foreground structure or the background model. In this paper, we handle these unexplained data explicitly and address the moving target detection from space as one of the pioneer studies. We propose a technique by extending the decomposition formulation with bounded errors, named Extended Low-rank and Structured Sparse Decomposition (E-LSD). This formulation integrates low-rank background, structured sparse foreground and their residuals in a matrix decomposition problem. We provide an effective solution by introducing an alternative treatment and adopting the direct extension of Alternating Direction Method of Multipliers (ADMM). The proposed E-LSD was validated on two satellite videos, and experimental results demonstrate the improvement in background modeling with boosted moving object detection precision over state-of-the-art methods.
Tasks	Object Detection
Published	2019-08-26
URL	https://arxiv.org/abs/1908.09539v1
PDF	https://arxiv.org/pdf/1908.09539v1.pdf
PWC	https://paperswithcode.com/paper/error-bounded-foreground-and-background
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Speaker-independent classification of phonetic segments from raw ultrasound in child speech


Title	Speaker-independent classification of phonetic segments from raw ultrasound in child speech
Authors	Manuel Sam Ribeiro, Aciel Eshky, Korin Richmond, Steve Renals
Abstract	Ultrasound tongue imaging (UTI) provides a convenient way to visualize the vocal tract during speech production. UTI is increasingly being used for speech therapy, making it important to develop automatic methods to assist various time-consuming manual tasks currently performed by speech therapists. A key challenge is to generalize the automatic processing of ultrasound tongue images to previously unseen speakers. In this work, we investigate the classification of phonetic segments (tongue shapes) from raw ultrasound recordings under several training scenarios: speaker-dependent, multi-speaker, speaker-independent, and speaker-adapted. We observe that models underperform when applied to data from speakers not seen at training time. However, when provided with minimal additional speaker information, such as the mean ultrasound frame, the models generalize better to unseen speakers.
Tasks
Published	2019-07-01
URL	https://arxiv.org/abs/1907.01413v1
PDF	https://arxiv.org/pdf/1907.01413v1.pdf
PWC	https://paperswithcode.com/paper/speaker-independent-classification-of
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Deep Level Sets: Implicit Surface Representations for 3D Shape Inference


Title	Deep Level Sets: Implicit Surface Representations for 3D Shape Inference
Authors	Mateusz Michalkiewicz, Jhony K. Pontes, Dominic Jack, Mahsa Baktashmotlagh, Anders Eriksson
Abstract	This repository contains the code for the paper “Occupancy Networks - Learning 3D Reconstruction in Function Space”
Tasks	3D Reconstruction, 3D Shape Representation
Published	2019-01-21
URL	http://arxiv.org/abs/1901.06802v1
PDF	http://arxiv.org/pdf/1901.06802v1.pdf
PWC	https://paperswithcode.com/paper/deep-level-sets-implicit-surface
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