paper_name stringlengths 4 421 | paper_url stringlengths 21 200 | paper_authors listlengths 0 125 | paper_abstract stringlengths 0 43.4k | paper_code stringlengths 1 149 | conf stringlengths 6 18 |
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A Penalized Autoencoder Approach for Nonlinear Independent Component Analysis | https://doi.org/10.1109/ICASSP.2019.8682469 | [
"Tianwen Wei",
"Stéphane Chrétien"
] | We propose Independent Component Autoencoder (ICAE), a deep neural network-based framework for nonlinear Independent Component Analysis (ICA). The proposed method consists of a penalized autoencoder and a training objective that is to minimize a combination of the reconstruction loss and an ICA contrast. Unlike many pr... | # | ICASSP2019 |
Prune Your Neurons Blindly: Neural Network Compression through Structured Class-blind Pruning | https://doi.org/10.1109/ICASSP.2019.8683224 | [
"Abdullah Salama",
"Oleksiy Ostapenko",
"Tassilo Klein",
"Moin Nabi"
] | High performance of deep learning models typically comes at cost of considerable model size and computation time. These factors limit applicability for deployment on memory and battery constrained devices such as mobile phones or embedded systems. In this work, we propose a novel pruning technique that eliminates entir... | # | ICASSP2019 |
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification | https://doi.org/10.1109/ICASSP.2019.8683197 | [
"Xiaofeng Zhang",
"Zhangyang Wang",
"Dong Liu",
"Qing Ling"
] | Deep learning has revolutionized the performance of classification, but meanwhile demands sufficient labeled data for training. Given insufficient data, while many techniques have been developed to help combat overfitting, the challenge remains if one tries to train deep networks, especially in the ill-posed extremely ... | https://github.com/SchafferZhang/DADA | ICASSP2019 |
Understanding Deep Neural Networks through Input Uncertainties | https://doi.org/10.1109/ICASSP.2019.8682930 | [
"Jayaraman J. Thiagarajan",
"Irene Kim",
"Rushil Anirudh",
"Peer-Timo Bremer"
] | Techniques for understanding the functioning of complex machine learning models are becoming increasingly popular, not only to improve the validation process, but also to extract new insights about the data via exploratory analysis. Though a large class of such tools currently exists, most assume that predictions are p... | # | ICASSP2019 |
HMM-based Approaches to Model Multichannel Information in Sign Language Inspired from Articulatory Features-based Speech Processing | https://doi.org/10.1109/ICASSP.2019.8683167 | [
"Sandrine Tornay",
"Marzieh Razavi",
"Necati Cihan Camgöz",
"Richard Bowden",
"Mathew Magimai-Doss"
] | Sign language conveys information through multiple channels, such as hand shape, hand movement, and mouthing. Modeling this multichannel information is a highly challenging problem. In this paper, we elucidate the link between spoken language and sign language in terms of production phenomenon and perception phenomenon... | # | ICASSP2019 |
Speech Emotion Recognition Using Multi-hop Attention Mechanism | https://doi.org/10.1109/ICASSP.2019.8683483 | [
"Seunghyun Yoon",
"Seokhyun Byun",
"Subhadeep Dey",
"Kyomin Jung"
] | In this paper, we are interested in exploiting textual and acoustic data of an utterance for the speech emotion classification task. The baseline approach models the information from audio and text independently using two deep neural networks (DNNs). The outputs from both the DNNs are then fused for classification. As ... | # | ICASSP2019 |
How Transferable Are Features in Convolutional Neural Network Acoustic Models across Languages? | https://doi.org/10.1109/ICASSP.2019.8683043 | [
"Jessica A. F. Thompson",
"Marc Schönwiesner",
"Yoshua Bengio",
"Daniel Willett"
] | Characterization of the representations learned in intermediate layers of deep networks can provide valuable insight into the nature of a task and can guide the development of well-tailored learning strategies. Here we study convolutional neural network (CNN)-based acoustic models in the context of automatic speech rec... | # | ICASSP2019 |
Gradient-based Active Learning Query Strategy for End-to-end Speech Recognition | https://doi.org/10.1109/ICASSP.2019.8683089 | [
"Yang Yuan",
"Soo-Whan Chung",
"Hong-Goo Kang"
] | In this paper, we propose an effective active learning query strategy for an automatic speech recognition system with the aim of reducing the training cost. Generally, training a deep neural network with supervised learning requires a massive amount of labeled data to obtain excellent performance. However, labeling dat... | # | ICASSP2019 |
Semi-supervised Training for End-to-end Models via Weak Distillation | https://doi.org/10.1109/ICASSP.2019.8682172 | [
"Bo Li",
"Tara N. Sainath",
"Ruoming Pang",
"Zelin Wu"
] | End-to-end (E2E) models are a promising research direction in speech recognition, as the single all-neural E2E system offers a much simpler and more compact solution compared to a conventional model, which has a separate acoustic (AM), pronunciation (PM) and language model (LM). However, it has been noted that E2E mode... | # | ICASSP2019 |
Stochastic Adaptive Neural Architecture Search for Keyword Spotting | https://doi.org/10.1109/ICASSP.2019.8683305 | [
"Tom Véniat",
"Olivier Schwander",
"Ludovic Denoyer"
] | The problem of keyword spotting i.e. identifying keywords in a real-time audio stream is mainly solved by applying a neural network over successive sliding windows. Due to the difficulty of the task, baseline models are usually large, resulting in a high computational cost and energy consumption level. We propose a new... | https://github.com/TomVeniat/SANAS | ICASSP2019 |
Bias Mitigation Post-processing for Individual and Group Fairness | https://doi.org/10.1109/ICASSP.2019.8682620 | [
"Pranay Kr. Lohia",
"Karthikeyan Natesan Ramamurthy",
"Manish Bhide",
"Diptikalyan Saha",
"Kush R. Varshney",
"Ruchir Puri"
] | Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness. Our novel framework includes an individual bias detector used to prioritize data samples in a bias mitigation a... | # | ICASSP2019 |
Inference about Causality from Cardiotocography Signals Using Gaussian Processes | https://doi.org/10.1109/ICASSP.2019.8683052 | [
"Guanchao Feng",
"J. Gerald Quirk",
"Petar M. Djuric"
] | In this paper, we propose a novel and simple method for discovery of Granger causality from noisy time series using Gaussian processes. More specifically, we adopt the concept of Granger causality, but instead of using autoregressive models for establishing it, we work with Gaussian processes. We show that information ... | # | ICASSP2019 |
Convergence Bounds for Compressed Gradient Methods with Memory Based Error Compensation | https://doi.org/10.1109/ICASSP.2019.8682931 | [
"Sarit Khirirat",
"Sindri Magnússon",
"Mikael Johansson"
] | The veritable scale of modern data necessitates information compression in parallel/distributed big-data optimization. Compression schemes using memory-based error compensation have displayed superior performance in practice, however, to date there are no theoretical explanations for these observed advantages. This pap... | # | ICASSP2019 |
Efficient Stochastic Subgradient Descent Algorithms for High-dimensional Semi-sparse Graphical Model Selection | https://doi.org/10.1109/ICASSP.2019.8683823 | [
"Songwei Wu",
"Hang Yu",
"Justin Dauwels"
] | We consider the structure learning problem of Gaussian graphical models when the underlying graph is semi-sparse. More specifically, we assume that the number of edges in the graph grows quadratically with the dimension P . Similar to the case of sparse graphs, the problem is formulated as maximizing the data log-likel... | # | ICASSP2019 |
Anomaly Detection in Single Subject vs Group Using Manifold Learning | https://doi.org/10.1109/ICASSP.2019.8682472 | [
"Florian Tilquin",
"Sylvain Faisan",
"Fabrice Heitz",
"Vincent Noblet",
"Frédéric Blanc",
"Izzie Namer"
] | This paper compares several linear and non-linear multivariate models for the detection of abnormal patterns in neuroimaging data, when comparing a single subject to a normal control group. The proposed methods learn the manifold spanned by the normal controls using non-linear dimension reduction techniques. The image ... | # | ICASSP2019 |
Detecting Cyber Attacks Using Anomaly Detection with Explanations and Expert Feedback | https://doi.org/10.1109/ICASSP.2019.8683212 | [
"Md Amran Siddiqui",
"Jack W. Stokes",
"Christian Seifert",
"Evan Argyle",
"Robert McCann",
"Joshua Neil",
"Justin Carroll"
] | Detecting cyber attacks in large computer networks is crucial for many organizations. To that purpose, different types of detectors capture the important signals resembling a security attack from individual computers and bring that to the attention of a security analyst. Unfortunately, the analyst sometimes has no indi... | # | ICASSP2019 |
Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data | https://doi.org/10.1109/ICASSP.2019.8682986 | [
"Philipp Koch",
"Nele Sophie Brügge",
"Huy Phan",
"Marco Maaß",
"Alfred Mertins"
] | For many applications of hand gesture recognition, a delay-free, affordable, and mobile system relying on body signals is mandatory. Therefore, we propose an approach for hand gestures classification given signals of inertial measurement units (IMUs) that works with extremely short windows to avoid delays. With a simpl... | # | ICASSP2019 |
Missing Data in Traffic Estimation: A Variational Autoencoder Imputation Method | https://doi.org/10.1109/ICASSP.2019.8683011 | [
"Guillem Boquet",
"José López Vicario",
"Antoni Morell",
"Javier Serrano"
] | Road traffic forecasting systems are in scenarios where sensor or system failure occur. In those scenarios, it is known that missing values negatively affect estimation accuracy although it is being often underestimate in current deep neural network approaches. Our assumption is that traffic data can be generated from ... | # | ICASSP2019 |
Surgical Activities Recognition Using Multi-scale Recurrent Networks | https://doi.org/10.1109/ICASSP.2019.8683849 | [
"Ilker Gurcan",
"Hien Van Nguyen"
] | Recently, surgical activity recognition has been receiving significant attention from the medical imaging community. Existing state-of-the-art approaches employ recurrent neural networks such as long-short term memory networks (LSTMs). However, our experiments show that these networks are not effective in capturing the... | # | ICASSP2019 |
Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration | https://doi.org/10.1109/ICASSP.2019.8682484 | [
"Houpu Yao",
"Jingjing Wen",
"Yi Ren",
"Bin Wu",
"Ze Ji"
] | Special high-end sensors with expensive hardware are usually needed to measure shock signals with high accuracy. In this paper, we show that cheap low-end sensors calibrated by deep neural networks are also capable to measure high-g shocks accurately. Firstly we perform drop shock tests to collect a dataset of shock si... | # | ICASSP2019 |
Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning | https://doi.org/10.1109/ICASSP.2019.8682555 | [
"Yue Jin",
"Yaodong Zhang",
"Jian Yuan",
"Xudong Zhang"
] | This work addresses a multi-agent cooperative navigation problem that multiple agents work together in an unknown environment in order to reach different targets without collision and minimize the maximum navigation time they spend. Typical reinforcement learning-based solutions directly model the cooperative navigatio... | # | ICASSP2019 |
Deep Complex-valued Neural Beamformers | https://doi.org/10.1109/ICASSP.2019.8683517 | [
"Lukas Pfeifenberger",
"Matthias Zöhrer",
"Franz Pernkopf"
] | We propose a complex-valued deep neural network (cDNN) for speech enhancement and source separation. While existing end-to-end systems use complex-valued gradients to pass the training error to a real-valued DNN used for gain mask estimation, we use the full potential of complex-valued LSTMs, MLPs and activation functi... | # | ICASSP2019 |
Fast and Global Optimal Nonconvex Matrix Factorization via Perturbed Alternating Proximal Point | https://doi.org/10.1109/ICASSP.2019.8682941 | [
"Songtao Lu",
"Mingyi Hong",
"Zhengdao Wang"
] | In this paper, we use the perturbed gradient based alternating minimization for solving a class of low-rank matrix factorization problems. Alternating minimization is a simple but popular approach which has been applied to problems in optimization, machine learning, data mining, and signal processing, etc. By leveragin... | # | ICASSP2019 |
Statistical Rank Selection for Incomplete Low-rank Matrices | https://doi.org/10.1109/ICASSP.2019.8682540 | [
"Rui Zhang",
"Alexander Shapiro",
"Yao Xie"
] | We consider the problem of determining the rank in the low-rank matrix completion. We propose a statistical model for noisy observation. It is important for many existing algorithms and sometimes has practical meanings. We construct a test statistics for the low rank approximation problem. Under this model, we derive t... | # | ICASSP2019 |
Accelerating Iterative Hard Thresholding for Low-rank Matrix Completion via Adaptive Restart | https://doi.org/10.1109/ICASSP.2019.8683082 | [
"Trung Vu",
"Raviv Raich"
] | This paper introduces the use of adaptive restart to accelerate iterative hard thresholding (IHT) for low-rank matrix completion. First, we analyze the local convergence of accelerated IHT in the non-convex setting of matrix completion problem (MCP). We prove the linear convergence rate of the accelerated algorithm ins... | # | ICASSP2019 |
Latent Schatten TT Norm for Tensor Completion | https://doi.org/10.1109/ICASSP.2019.8683685 | [
"Andong Wang",
"Xulin Song",
"Xiyin Wu",
"Zhihui Lai",
"Zhong Jin"
] | Tensor completion arouses much attention in signal processing and machine learning. The tensor train (TT) decomposition has shown better performances than the Tucker decomposition in image and video inpainting. In this paper, we propose a novel tensor completion model based on a newly defined latent Schatten TT norm. T... | # | ICASSP2019 |
Common Mode Patterns for Supervised Tensor Subspace Learning | https://doi.org/10.1109/ICASSP.2019.8682616 | [
"Konstantinos Makantasis",
"Anastasios D. Doulamis",
"Nikolaos Doulamis",
"Athanasios Voulodimos"
] | In this work we propose a method for reducing the dimensionality of tensor objects in a binary classification framework. The proposed Common Mode Patterns method takes into consideration the labels’ information, and ensures that tensor objects that belong to different classes do not share common features after the redu... | # | ICASSP2019 |
Regular Sampling of Tensor Signals: Theory and Application to FMRI | https://doi.org/10.1109/ICASSP.2019.8682230 | [
"Charilaos I. Kanatsoulis",
"Nicholas D. Sidiropoulos",
"Mehmet Akçakaya",
"Xiao Fu"
] | Sampling lies at the heart of signal processing. The celebrated Shan-non - Nyquist theorem states that in order to reconstruct a continuous or discrete time signal from uniform samples one must sample at a rate twice the highest frequency present in the signal. Numerous signals and images of interest, however, are not ... | # | ICASSP2019 |
Learning Laplacian Matrix from Bandlimited Graph Signals | https://doi.org/10.1109/ICASSP.2019.8682769 | [
"Batiste Le Bars",
"Pierre Humbert",
"Laurent Oudre",
"Argyris Kalogeratos"
] | In this paper, we present a method for learning an underlying graph topology using observed graph signals as training data. The novelty of our method lies on the combination of two assumptions that are imposed as constraints to the graph learning process: i) the standard assumption used in the literature that signals a... | # | ICASSP2019 |
Community Detection in Sparse Realistic Graphs: Improving the Bethe Hessian | https://doi.org/10.1109/ICASSP.2019.8683594 | [
"Lorenzo Dall'Amico",
"Romain Couillet"
] | This article improves over the recently proposed Bethe Hessian matrix for community detection on sparse graphs, assuming here a more realistic setting where node degrees are inhomogeneous. We notably show that the parametrization proposed in the seminal work on the Bethe Hessian clustering can be ameliorated with posit... | # | ICASSP2019 |
Multiview Canonical Correlation Analysis over Graphs | https://doi.org/10.1109/ICASSP.2019.8683096 | [
"Jia Chen",
"Gang Wang",
"Georgios B. Giannakis"
] | Multiview canonical correlation analysis (MCCA) looks for shared low-dimensional representations hidden in multiple transformations of common source signals. Existing MCCA approaches do not exploit the geometry of common sources, which can be either given a priori, or constructed from do- main knowledge. In this paper,... | # | ICASSP2019 |
Graphical Lasso for High-dimensional Complex Gaussian Graphical Model Selection | https://doi.org/10.1109/ICASSP.2019.8682867 | [
"Jitendra K. Tugnait"
] | We consider the problem of infemng the conditional independence graph (CIG) of both proper and improper, complex-valued, high- dimensional multivariate Gaussian vectors. A $p$-variate complex Gaussian graphical model (CGGM) associated with an undirected graph with $p$ vemces is defined as the family of complex Gaussian... | # | ICASSP2019 |
Deepwalk-assisted Graph PCA (DGPCA) for Language Networks | https://doi.org/10.1109/ICASSP.2019.8682615 | [
"Fenxiao Chen",
"Bin Wang",
"C.-C. Jay Kuo"
] | Language graph learning is an important task with many applications such as text classification, link prediction and community detection. One of the challenges in this domain is finding an efficient way to learn and encode graph into a low dimensional embedding. In this paper, a novel DeepWalk-assisted Graph PCA (DGPCA... | # | ICASSP2019 |
Scalable Mutual Information Estimation Using Dependence Graphs | https://doi.org/10.1109/ICASSP.2019.8683351 | [
"Morteza Noshad",
"Yu Zeng",
"Alfred O. Hero III"
] | The Mutual Information (MI) is an often used measure of dependency between two random variables utilized in informa- tion theory, statistics and machine learning. Recently several MI estimators have been proposed that can achieve paramet- ric MSE convergence rate. However, most of the previously proposed estimators hav... | # | ICASSP2019 |
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables | https://doi.org/10.1109/ICASSP.2019.8683641 | [
"Shaojie Xu",
"Sihan Zeng",
"Justin Romberg"
] | Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction from compressed measurements with image priors captured by a generative model. We search and constrain on latent variable space to make the m... | https://github.com/sihan-zeng/f-csrg | ICASSP2019 |
Robust Dictionary Learning Using α-Divergence | https://doi.org/10.1109/ICASSP.2019.8683210 | [
"Asif Iqbal",
"Abd-Krim Seghouane"
] | In this paper, a robust sequential dictionary learning (DL) algorithm is presented. It is obtained by using a robust loss function in the data fidelity term of the DL objective instead of the usual quadratic loss. The proposed robust loss func- tion is derived from the α -divergence as an alternative to the Kullback-Le... | # | ICASSP2019 |
Simultaneous Blind Deconvolution and Phase Retrieval with Tensor Iterative Hard Thresholding | https://doi.org/10.1109/ICASSP.2019.8683575 | [
"Shuang Li",
"Gongguo Tang",
"Michael B. Wakin"
] | Blind deconvolution and phase retrieval are both fundamental problems with a growing interest in signal processing and communications. In this work, we consider the task of simultaneous blind deconvolution and phase retrieval. We show that this non-linear problem can be reformulated as a low-rank tensor recovery proble... | # | ICASSP2019 |
FirmNet: A Sparsity Amplified Deep Network for Solving Linear Inverse Problems | https://doi.org/10.1109/ICASSP.2019.8682792 | [
"Praveen Kumar Pokala",
"Amol G. Mahurkar",
"Chandra Sekhar Seelamantula"
] | Recovering a sparse signal from a noisy linear measurement is an important problem in signal processing. Typically, one employs greedy pursuit techniques such as OMP, CoSaMP to solve an ${\ell _0}$ regular- ization problem. For large-scale problems, iterative shrinkage tech- niques such as ISTA, FISTA, ${\text{AMP}} - ... | # | ICASSP2019 |
Deep Signal Recovery with One-bit Quantization | https://doi.org/10.1109/ICASSP.2019.8683876 | [
"Shahin Khobahi",
"Naveed Naimipour",
"Mojtaba Soltanalian",
"Yonina C. Eldar"
] | Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference. In this paper, we consider the application of the deep unfolding technique in the problem of signal reconstruction from its one-bit noisy measurements. Namely, we propose a model-based mac... | # | ICASSP2019 |
Bayesian Neural Networks for Sparse Coding | https://doi.org/10.1109/ICASSP.2019.8682174 | [
"Danil Kuzin",
"Olga Isupova",
"Lyudmila Mihaylova"
] | Deep learning is actively used in the area of sparse coding. In current deep sparse coding methods uncertainty of predictions is rarely estimated, thus providing the results that lack the quantitative justification. Bayesian learning provides the way to estimate the uncertainty of predictions in neural networks (NNs) b... | # | ICASSP2019 |
Neural CRF Transducers for Sequence Labeling | https://doi.org/10.1109/ICASSP.2019.8682728 | [
"Kai Hu",
"Zhijian Ou",
"Min Hu",
"Junlan Feng"
] | Conditional random fields (CRFs) have been shown to be one of the most successful approaches to sequence labeling. Various linear-chain neural CRFs (NCRFs) are developed to implement the non-linear node potentials in CRFs, but still keeping the linear-chain hidden structure. In this paper, we propose NCRF transducers, ... | # | ICASSP2019 |
Neural Networks Sequential Training Using Variational Gaussian Particle Filter | https://doi.org/10.1109/ICASSP.2019.8683886 | [
"Mhd Modar Halimeh",
"Andreas Brendel",
"Walter Kellermann"
] | In this paper, we propose a sequential training algorithm for feed-forward neural networks based on particle filtering. The proposed algorithm uses variational learning to tailor a proposal density by minimizing the variational energy. This density is then incorporated into the Gaussian particle filter framework. The p... | # | ICASSP2019 |
Unsupervised Person Re-identification Using Reliable and Soft Labels | https://doi.org/10.1109/ICASSP.2019.8683353 | [
"Jun Sun",
"Cheolkon Jung"
] | In this paper, we propose unsupervised person re-identification (ReID) using reliable and soft labels. We provide unsupervised person ReID to consider unknown pedestrian queries. We update ResNet model for person ReID based on reliable and soft labels. First, we perform unsupervised clustering on person images under di... | # | ICASSP2019 |
Hyperspectral Image Super-resolution Using Generative Adversarial Network and Residual Learning | https://doi.org/10.1109/ICASSP.2019.8683893 | [
"Qian Huang",
"Wei Li",
"Ting Hu",
"Ran Tao"
] | Due to the limitation of image acquisition, hyperspectral remote sensing imagery is hard to reflect in both high spatial and spectral resolutions. Super-resolution (SR) is a technique which can improve the spatial resolution. Inspired by recent achievements in deep convolutional neural network (CNN) and generative adve... | # | ICASSP2019 |
Towards Visually Grounded Sub-word Speech Unit Discovery | https://doi.org/10.1109/ICASSP.2019.8682666 | [
"David Harwath",
"James R. Glass"
] | In this paper, we investigate the manner in which interpretable sub-word speech units emerge within a convolutional neural network model trained to associate raw speech waveforms with semantically related natural image scenes. We show how diphone boundaries can be superficially extracted from the activation patterns of... | # | ICASSP2019 |
Low-rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling | https://doi.org/10.1109/ICASSP.2019.8682265 | [
"Chao Li",
"Zhun Sun",
"Jinshi Yu",
"Ming Hou",
"Qibin Zhao"
] | Although the convolutional neural networks (CNNs) have become popular for various image processing and computer vision tasks recently, it remains a challenging problem to reduce the storage cost of the parameters for resource-limited platforms. In the previous studies, tensor decomposition (TD) has achieved promising c... | # | ICASSP2019 |
End-to-end Change Detection Using a Symmetric Fully Convolutional Network for Landslide Mapping | https://doi.org/10.1109/ICASSP.2019.8682802 | [
"Tao Lei",
"Qi Zhang",
"Dinghua Xue",
"Tao Chen",
"Hongying Meng",
"Asoke K. Nandi"
] | In this paper, we propose a novel approach based on a symmetric fully convolutional network within pyramid pooling (FCN-PP) for landslide mapping (LM). The proposed approach has three advantages. Firstly, this approach is automatic and insensitive to noise because multivariate morphological reconstruction (MMR) is used... | # | ICASSP2019 |
Optimizing QoE of Multiple Users over DASH: A Meta-learning Approach | https://doi.org/10.1109/ICASSP.2019.8683246 | [
"Liangyu Huo",
"Zulin Wang",
"Mai Xu",
"Zhiguo Ding",
"Xiaoming Tao"
] | Dynamic adaptive video streaming over HTTP (DASH) plays a key role in video transmission over the Internet. The conventional DASH adaptation approaches concentrate on optimizing the overall quality of experience (QoE) for all client sides, neglecting the QoE diversity of different users. In this paper, we formulate the... | # | ICASSP2019 |
Large-pose Face Alignment via Shape-aware Heatmap | https://doi.org/10.1109/ICASSP.2019.8682447 | [
"Jiaxin Si",
"Fei Jiang",
"Ruimin Shen"
] | In this paper, we focus on dealing with problems of large-pose face alignment. Recently proposed heatmap-based algorithms have made promising performance on this problem. However, the traditional heatmap is constructed based on Gaussian model with fixed variance, which is inconsistent with the local shape of faces. In ... | # | ICASSP2019 |
Non-local Self-attention Structure for Function Approximation in Deep Reinforcement Learning | https://doi.org/10.1109/ICASSP.2019.8682832 | [
"Zhixiang Wang",
"Xi Xiao",
"Guangwu Hu",
"Yao Yao",
"Dianyan Zhang",
"Zhendong Peng",
"Qing Li",
"Shutao Xia"
] | Reinforcement learning is a framework to make sequential decisions. The combination with deep neural networks further improves the ability of this framework. Convolutional nerual networks make it possible to make sequential decisions based on raw pixels information directly and make reinforcement learning achieve satis... | # | ICASSP2019 |
A Two-class Hyper-spherical Autoencoder for Supervised Anomaly Detection | https://doi.org/10.1109/ICASSP.2019.8683790 | [
"Yuta Kawachi",
"Yuma Koizumi",
"Shin Murata",
"Noboru Harada"
] | Supervised anomaly detection has been a tough problem due to its necessity of special handling of unseen anomalies. In this paper, we present a heuristic implementation of variational auto-encoder with von-Mises Fisher prior applied to a supervised anomaly detector. The closed latent space like sphere is suitable for d... | # | ICASSP2019 |
Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection | https://doi.org/10.1109/ICASSP.2019.8682483 | [
"Cemre Zor",
"Muhammad Awais",
"Josef Kittler",
"Miroslaw Bober",
"Sameed Husain",
"Qiuqiang Kong",
"Christian Kroos"
] | In this paper, we address the problem of bird audio detection and propose a new convolutional neural network architecture together with a divergence based information channel weighing strategy in order to achieve improved state-of-the-art performance and faster convergence. The effectiveness of the methodology is shown... | # | ICASSP2019 |
Reinforcement Learning with Safe Exploration for Network Security | https://doi.org/10.1109/ICASSP.2019.8682983 | [
"Canhuang Dai",
"Liang Xiao",
"Xiaoyue Wan",
"Ye Chen"
] | Safe reinforcement learning is important for the safety critical applications especially network security, as the exploration of some dangerous actions can result in huge short-term losses such as network failure or large scale privacy leakage. In this paper, we propose a reinforcement learning algorithm with safe expl... | # | ICASSP2019 |
Team Policy Learning for Multi-agent Reinforcement Learning | https://doi.org/10.1109/ICASSP.2019.8683168 | [
"Lucas Cassano",
"Sulaiman A. Alghunaim",
"Ali H. Sayed"
] | This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on games that can only be solved through coordinated team work. We consider situations in which $K$ players interact simultaneously with an environment and with each ot... | # | ICASSP2019 |
Deep Reinforcement Learning for Financial Trading Using Price Trailing | https://doi.org/10.1109/ICASSP.2019.8683161 | [
"Konstantinos Saitas Zarkias",
"Nikolaos Passalis",
"Avraam Tsantekidis",
"Anastasios Tefas"
] | Developing accurate financial analysis tools can be useful both for speculative trading, as well as for analyzing the behavior of markets and promptly responding to unstable conditions ensuring the smooth operation of the financial markets. This led to the development of various methods for analyzing and forecasting th... | # | ICASSP2019 |
Joint On-line Learning of a Zero-shot Spoken Semantic Parser and a Reinforcement Learning Dialogue Manager | https://doi.org/10.1109/ICASSP.2019.8683274 | [
"Matthieu Riou",
"Bassam Jabaian",
"Stéphane Huet",
"Fabrice Lefèvre"
] | Despite many recent advances for the design of dialogue systems, a true bottleneck remains the acquisition of data required to train its components. Unlike many other language processing applications, dialogue systems require interactions with users, therefore it is complex to develop them with pre-recorded data. Build... | # | ICASSP2019 |
Graph Signal Sampling via Reinforcement Learning | https://doi.org/10.1109/ICASSP.2019.8683181 | [
"Oleksii Abramenko",
"Alexander Jung"
] | We model the sampling and recovery of clustered graph signals as a reinforcement learning (RL) problem. The signal sampling is carried out by an agent which crawls over the graph and selects the most relevant graph nodes to sample. The goal of the agent is to select signal samples which allow for the most accurate reco... | # | ICASSP2019 |
Bhattacharyya Distance-based Transfer Learning for a Hybrid EEG-FTCD Brain-computer Interface | https://doi.org/10.1109/ICASSP.2019.8683308 | [
"Elise Dagois",
"Aya Khalaf",
"Ervin Sejdic",
"Murat Akçakaya"
] | In this paper, we introduce a transfer learning approach for our novel hybrid brain-computer interface in which electroencephalography and functional transcranial Doppler ultrasound are used simultaneously to record brain electrical activity and cerebral blood velocity respectively due to flickering mental rotation and... | # | ICASSP2019 |
A Subject-to-subject Transfer Learning Framework Based on Jensen-Shannon Divergence for Improving Brain-computer Interface | https://doi.org/10.1109/ICASSP.2019.8683331 | [
"Joshua Giles",
"Kai Keng Ang",
"Lyudmila S. Mihaylova",
"Mahnaz Arvaneh"
] | One of the major limitations of current electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the long calibration time. Due to a high level of noise and non-stationarity inherent in EEG signals, a calibration model trained using limited number of train data may not yield an accurate BCI model. To addres... | # | ICASSP2019 |
Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks | https://doi.org/10.1109/ICASSP.2019.8682841 | [
"Navneet Garg",
"Mathini Sellathurai",
"Tharmalingam Ratnarajah"
] | To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning... | # | ICASSP2019 |
Active Learning with Label Proportions | https://doi.org/10.1109/ICASSP.2019.8682748 | [
"Rafael Poyiadzi",
"Raúl Santos-Rodríguez",
"Niall Twomey"
] | Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy an... | # | ICASSP2019 |
COVER: A Cluster-based Variance Reduced Method for Online Learning | https://doi.org/10.1109/ICASSP.2019.8682527 | [
"Kun Yuan",
"Bicheng Ying",
"Ali H. Sayed"
] | In this paper, we develop a stochastic-gradient learning algorithm for situations involving streaming data that arise from an underlying clustered structure. In such settings, the variance of gradient noise can be decomposed into the in-cluster variance $\sigma _{{\text{in}}}^2$ plus the between- cluster variance $\sig... | # | ICASSP2019 |
Flexible Non-negative Matrix Factorization with Adaptively Learned Graph Regularization | https://doi.org/10.1109/ICASSP.2019.8683840 | [
"Yong Peng",
"Yanfang Long",
"Feiwei Qin",
"Wanzeng Kong",
"Feiping Nie",
"Andrzej Cichocki"
] | Non-negative matrix factorization (NMF) is an efficient model in learning parts-based data representation. Since the local geometrical structure can be effectively modeled by a nearest neighbor graph, the graph regularized NMF (GNMF) was proposed to make the learned representation more faithfully and better characteriz... | # | ICASSP2019 |
Distributed Differentially-private Canonical Correlation Analysis | https://doi.org/10.1109/ICASSP.2019.8683252 | [
"Hafiz Imtiaz",
"Anand D. Sarwate"
] | We propose a distributed differentially-private canonical correlation analysis (CCA) algorithm to use on multi-view data. CCA finds a subspace for each view such that projecting the views onto these subspaces simultaneously reduces the dimension and maximizes correlation. In applications involving privacy-sensitive dat... | # | ICASSP2019 |
A Deep Neural Network Based Maneuvering-target Tracking Algorithm | https://doi.org/10.1109/ICASSP.2019.8683888 | [
"Jingxian Liu",
"Zulin Wang",
"Mai Xu",
"Jie Ren"
] | In the field of maneuvering-target tracking (MTT), the targets with changeable and uncertain maneuvering movements cannot be tracked precisely because there always exist time delays of maneuvering model estimation with traditional MT-T algorithms. To solve this problem, we propose a deep MTT (DeepMTT) algorithm based o... | # | ICASSP2019 |
Learning to Fuse Latent Representations for Multimodal Data | https://doi.org/10.1109/ICASSP.2019.8682454 | [
"Oyebade K. Oyedotun",
"Djamila Aouada",
"Björn E. Ottersten"
] | Multimodal learning leverages data from different modalities to improve the performance of a trained model. Typically, latent representations extracted from multimodal data are provided via direct feature fusion for end-to-end training of a deep neural network towards a specific task. However, the informativeness of th... | # | ICASSP2019 |
Feature Selection for Mutlti-labeled Variables via Dependency Maximization | https://doi.org/10.1109/ICASSP.2019.8682529 | [
"Salimeh Yasaei Sekeh",
"Alfred O. Hero III"
] | Feature selection and reducing the dimensionality of data is an essential step in data analysis. In this work we propose a new criterion for feature selection that is formulated as conditional information between features given the labeled variable. Instead of using the standard mutual information measure based on Kull... | # | ICASSP2019 |
Pruning SIFT & SURF for Efficient Clustering of Near-duplicate Images | https://doi.org/10.1109/ICASSP.2019.8683078 | [
"Tushar Shankar Shinde",
"Anil Kumar Tiwari"
] | Clustering and categorization of similar images using SIFT and SURF require a high computational cost. In this paper, a simple approach to reduce the cardinality of keypoint set and prune the dimension of SIFT and SURF feature descriptors for efficient image clustering is proposed. For this purpose, sparsely spaced (un... | # | ICASSP2019 |
Automating the Classification of Urban Issue Reports: an Optimal Stopping Approach | https://doi.org/10.1109/ICASSP.2019.8682778 | [
"Yasitha Warahena Liyanage",
"Daphney-Stavroula Zois",
"Charalampos Chelmis",
"Mengfan Yao"
] | Empowering citizens to interact directly with their local governments through civic engagement platforms has emerged as an easy way to resolve urban issues. However, for authorities to manually process reported issues is both impractical and inefficient; accurate, online and near-real-time processing methods are necess... | # | ICASSP2019 |
Tensor-ring Nuclear Norm Minimization and Application for Visual : Data Completion | https://doi.org/10.1109/ICASSP.2019.8683115 | [
"Jinshi Yu",
"Chao Li",
"Qibin Zhao",
"Guoxu Zhao"
] | Tensor ring (TR) decomposition has been successfully used to obtain the state-of-the-art performance in the visual data completion problem. However, the existing TR-based completion methods are severely non-convex and computationally demanding. In addition, the determination of the optimal TR rank is a tough work in pr... | # | ICASSP2019 |
Supervised Kernel Change Point Detection with Partial Annotations | https://doi.org/10.1109/ICASSP.2019.8683471 | [
"Charles Truong",
"Laurent Oudre",
"Nicolas Vayatis"
] | In this article, we propose an automatic procedure to calibrate change point detection algorithms. Our approach expands on the ability of an expert to provide very rough segmentation estimates, called partial annotations, for a few signal examples. Our contribution consists in a supervised strategy to learn a kernel Ma... | # | ICASSP2019 |
Automatic Kernel Weighting for Multikernel Adaptive Filtering: Multiscale Aspects | https://doi.org/10.1109/ICASSP.2019.8682934 | [
"Kwangjin Jeong",
"Masahiro Yukawa"
] | This paper presents an automatic kernel weighting technique for multikernel adaptive filtering. The full potential of the multikernel adaptive filtering approach can only be achieved when the kernels are weighted appropriately. The proposed technique balances the dominance of the kernels by making the mean eigenvalues ... | # | ICASSP2019 |
Multiple-graph Recurrent Graph Convolutional Neural Network Architectures for Predicting Disease Outcomes | https://doi.org/10.1109/ICASSP.2019.8683433 | [
"Juliette Valenchon",
"Mark Coates"
] | Improving disease outcome prediction can greatly aid in the strategic deployment of secondary prevention approaches. We develop a method to predict the evolution of diseases by taking into account personal attributes of the subjects and their relationships with medical examination results. Our approach builds upon a re... | # | ICASSP2019 |
Joint Structured Graph Learning and Clustering Based on Concept Factorization | https://doi.org/10.1109/ICASSP.2019.8682779 | [
"Yong Peng",
"Rixin Tang",
"Wanzeng Kong",
"Jianhai Zhang",
"Feiping Nie",
"Andrzej Cichocki"
] | As one of the matrix factorization models, concept factorization (CF) achieved promising performance in learning data representation in both original feature space and reproducible kernel Hilbert space (RKHS). Based on the consensuses that 1) learning performance of models can be enhanced by exploiting the geometrical ... | # | ICASSP2019 |
Generative Graph Convolutional Network for Growing Graphs | https://doi.org/10.1109/ICASSP.2019.8682360 | [
"Da Xu",
"Chuanwei Ruan",
"Kamiya Motwani",
"Evren Körpeoglu",
"Sushant Kumar",
"Kannan Achan"
] | Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existing graph. Despite the emerging literature in learning graph representation and graph generation, most of them can not handle isolated new nod... | # | ICASSP2019 |
Unsupervised Feature Ranking and Selection Based on Autoencoders | https://doi.org/10.1109/ICASSP.2019.8682226 | [
"Sasan Sharifipour",
"Hossein Fayyazi",
"Mohammad Sabokrou",
"Ehsan Adeli"
] | Feature selection is one of the most important and widely-used dimension reduction techniques due to its efficiency and intractability of the results. In this paper, we propose a simple but efficient unsupervised feature ranking and selection method by exploiting the geometry of the original feature space using AutoEnc... | # | ICASSP2019 |
D2PGGAN: Two Discriminators Used in Progressive Growing of GANS | https://doi.org/10.1109/ICASSP.2019.8683262 | [
"Zhaoyu Zhang",
"Mengyan Li",
"Jun Yu"
] | Generative adversarial network (GAN) is a powerful generative model. However, it suffers from two key problems which are convergence instability and mode collapse. Recently, progressive growing of GANs for improving quality, stability and variation (PGGAN) is proposed to better solve these two problems. Although the pe... | # | ICASSP2019 |
Nonlinear Multi-scale Super-resolution Using Deep Learning | https://doi.org/10.1109/ICASSP.2019.8682354 | [
"Kenneth Tran",
"Ashkan Panahi",
"Aniruddha Adiga",
"Wesam A. Sakla",
"Hamid Krim"
] | We propose a deep learning architecture capable of performing up to 8× single image super-resolution. Our architecture incorporates an adversarial component from the super-resolution generative adversarial networks (SRGANs) and a multi-scale learning component from the multiple scale super-resolution network (MSSRNet),... | # | ICASSP2019 |
Dual-stream CNN for Structured Time Series Classification | https://doi.org/10.1109/ICASSP.2019.8682410 | [
"Shuchen Weng",
"Wenbo Li",
"Yi Zhang",
"Siwei Lyu"
] | The structured time series (STS) classification problem requires the modeling of interweaved spatiotemporal dependency. Most previous methods model these two dependencies independently. Due to the complexity of the STS data, we argue that a desirable method should be a holistic framework that is adaptive and flexible. ... | # | ICASSP2019 |
Learning Low Rank and Sparse Models via Robust Autoencoders | https://doi.org/10.1109/ICASSP.2019.8682925 | [
"Jie Pu",
"Yannis Panagakis",
"Maja Pantic"
] | Robust principal component analysis (RPCA), decomposes a data matrix into a superposition of a low-rank matrix and a sparse matrix under certain incoherent conditions. In this paper, we propose a nonlinear generalization of RPCA that uses two autoencoder networks to achieve such a decomposition, in which one autoencode... | # | ICASSP2019 |
Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification | https://doi.org/10.1109/ICASSP.2019.8683547 | [
"Rushil Anirudh",
"Jayaraman J. Thiagarajan"
] | Using predictive models to identify patterns that can act as biomarkers for different neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the problem of Autism Spectrum Disorder (ASD) classification where previous work has shown that it can be beneficial to incorporate a wide variety ... | # | ICASSP2019 |
Variational and Hierarchical Recurrent Autoencoder | https://doi.org/10.1109/ICASSP.2019.8683771 | [
"Jen-Tzung Chien",
"Chun-Wei Wang"
] | Despite a great success in learning representation for image data, it is challenging to learn the stochastic latent features from natural language based on variational inference. The difficulty in stochastic sequential learning is due to the posterior collapse caused by an autoregressive decoder which is prone to be to... | # | ICASSP2019 |
Deep Learning for Minimal-context Block Tracking through Side-channel Analysis | https://doi.org/10.1109/ICASSP.2019.8683845 | [
"L. Jensen",
"G. Brown",
"Xiao Wang",
"J. Harer",
"S. Chin"
] | It is well known that electromagnetic and power side-channel attacks allow extraction of unintended information from a computer processor. However, little work has been done to quantify how small a sample is needed in order to glean meaningful information about a program’s execution. This paper quantifies this minimum ... | # | ICASSP2019 |
Deep Convolutional Robust PCA with Application to Ultrasound Imaging | https://doi.org/10.1109/ICASSP.2019.8683030 | [
"Regev Cohen",
"Yi Zhang",
"Oren Solomon",
"Daniel Toberman",
"Liran Taieb",
"Ruud J. G. van Sloun",
"Yonina C. Eldar"
] | Sparse and low-rank decomposition, also known as robust principle component analysis, has been applied successfully in numerous applications. Typically, this approach leads to a minimization problem which is solved using iterative algorithms. Drawing inspiration from recurrent networks, in recent years deep-learning st... | # | ICASSP2019 |
Pigment Unmixing of Hyperspectral Images of Paintings Using Deep Neural Networks | https://doi.org/10.1109/ICASSP.2019.8682838 | [
"Neda Rohani",
"Emeline Pouyet",
"Marc Walton",
"Oliver Cossairt",
"Aggelos K. Katsaggelos"
] | In this paper, the problem of automatic nonlinear unmixing of hyperspectral reflectance data using works of art as test cases is described. We use a deep neural network to decompose a given spectrum quantitatively to the abundance values of pure pigments. We show that adding another step to identify the constituent pig... | # | ICASSP2019 |
Attention in Recurrent Neural Networks for Ransomware Detection | https://doi.org/10.1109/ICASSP.2019.8682899 | [
"Rakshit Agrawal",
"Jack W. Stokes",
"Karthik Selvaraj",
"Mady Marinescu"
] | Ransomware, as a specialized form of malicious software, has recently emerged as a major threat in computer security. With an ability to lock out user access to their content, recent ransomware attacks have caused severe impact at an individual and organizational level. While research in malware detection can be adapte... | # | ICASSP2019 |
Drawing Order Recovery for Handwriting Chinese Characters | https://doi.org/10.1109/ICASSP.2019.8683361 | [
"Bocheng Zhao",
"Minghao Yang",
"Jianhua Tao"
] | Recover drawing orders from a Chinese handwriting image is a challenge issue. Most of English drawing order recovery(DOR) methods perform unsatisfactorily in Chinese. This paper proposes a novel image-to-sequence algorithm to deal with Chinese DOR problem. The proposed method utilizes two regression convolution neural ... | # | ICASSP2019 |
Direct Estimation of Weights and Efficient Training of Deep Neural Networks without SGD | https://doi.org/10.1109/ICASSP.2019.8682781 | [
"Nima Dehmamy",
"Neda Rohani",
"Aggelos K. Katsaggelos"
] | We argue that learning a hierarchy of features in a hierarchical dataset requires lower layers to approach convergence faster than layers above them. We show that, if this assumption holds, we can analytically approximate the outcome of stochastic gradient descent (SGD) for each layer. We find that the weights should c... | # | ICASSP2019 |
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-encoders | https://doi.org/10.1109/ICASSP.2019.8683061 | [
"Hongyu Shen",
"Daniel George",
"Eliu A. Huerta",
"Zhizhen Zhao"
] | Denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc. For this task, a combination of a recurrent neural net (RNNs) with a Denoising Auto-Encoder (DAEs) has shown promising results. However, this combined model is challenged when operating with... | # | ICASSP2019 |
Deep Spline Networks with Control of Lipschitz Regularity | https://doi.org/10.1109/ICASSP.2019.8682547 | [
"Shayan Aziznejad",
"Michael Unser"
] | The motivation for this work is to improve the performance of deep neural networks through the optimization of the individual activation functions. Since the latter results in an infinite- dimensional optimization problem, we resolve the ambiguity by searching for the sparsest and most regular solution in the sense of ... | # | ICASSP2019 |
Deep Neural Networks for Low-resolution Photon-limited Imaging | https://doi.org/10.1109/ICASSP.2019.8682767 | [
"Omar DeGuchy",
"Fabian Santiago",
"Mario Banuelos",
"Roummel F. Marcia"
] | In this paper, we implement deep learning methods to recover downsampled noisy signals often present in compressed sensing applications. As an alternative to relying on previously established optimization based algorithms, we implement stacked denoising autoencoders and convolutional neural networks to perform signal r... | # | ICASSP2019 |
Adversarial Multi-label Prediction for Spoken and Visual Signal Tagging | https://doi.org/10.1109/ICASSP.2019.8683651 | [
"Yue Deng",
"KaWai Chen",
"Yilin Shen",
"Hongxia Jin"
] | We introduce an adversarial multi-label classification (ADMLC) framework to improve the robustness and performance of existing algorithms on multi-domain signals. The core contribution of our ADMLC is the innovation of an ‘adversarial module’ that serves as a critic to provide augmenting information to improve supervis... | # | ICASSP2019 |
Recurrent Deep Divergence-based Clustering for Simultaneous Feature Learning and Clustering of Variable Length Time Series | https://doi.org/10.1109/ICASSP.2019.8682365 | [
"Daniel J. Trosten",
"Andreas Storvik Strauman",
"Michael Kampffmeyer",
"Robert Jenssen"
] | The task of clustering unlabeled time series and sequences entails a particular set of challenges, namely to adequately model temporal relations and variable sequence lengths. If these challenges are not properly handled, the resulting clusters might be of suboptimal quality. As a key solution, we present a joint clust... | # | ICASSP2019 |
Embedding Physical Augmentation and Wavelet Scattering Transform to Generative Adversarial Networks for Audio Classification with Limited Training Resources | https://doi.org/10.1109/ICASSP.2019.8683199 | [
"Kah Kuan Teh",
"Tran Huy Dat"
] | This paper addresses audio classification with limited training resources. We first investigate different types of data augmentation including physical modeling, wavelet scattering transform and Generative Adversarial Networks (GAN). We than propose a novel GAN method to embed physical augmentation and wavelet scatteri... | # | ICASSP2019 |
Adversarial Inpainting of Medical Image Modalities | https://doi.org/10.1109/ICASSP.2019.8682677 | [
"Karim Armanious",
"Youssef Mecky",
"Sergios Gatidis",
"Bin Yang"
] | Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of me... | # | ICASSP2019 |
Towards Unsupervised Single-channel Blind Source Separation Using Adversarial Pair Unmix-and-remix | https://doi.org/10.1109/ICASSP.2019.8682375 | [
"Yedid Hoshen"
] | Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression ... | # | ICASSP2019 |
Efficient Randomized Defense against Adversarial Attacks in Deep Convolutional Neural Networks | https://doi.org/10.1109/ICASSP.2019.8683348 | [
"Fatemeh Sheikholeslami",
"Swayambhoo Jain",
"Georgios B. Giannakis"
] | Despite their well-documented learning capabilities in clean environments, deep convolutional neural networks (CNNs) are extremely fragile in adversarial settings, where carefully crafted perturbations created by an attacker can easily disrupt the task at hand. Numerous methods have been proposed for designing effectiv... | # | ICASSP2019 |
Postfiltering Using an Adversarial Denoising Autoencoder with Noise-aware Training | https://doi.org/10.1109/ICASSP.2019.8682684 | [
"Naohiro Tawara",
"Hikari Tanabe",
"Tetsunori Kobayashi",
"Masaru Fujieda",
"Kazuhiro Katagiri",
"Takashi Yazu",
"Tetsuji Ogawa"
] | An adversarial denoising autoencoder (ADAE) with noise-aware training is proposed and successfully applied to post-filtering for linear noise reduction. The ADAE is effective for attenuating interference sounds, however, it is difficult to learn to handle its various unexpected harmful effects (e.g., various types of n... | # | ICASSP2019 |
Adaptive Adjustment with Semantic Feature Space for Zero-shot Recognition | https://doi.org/10.1109/ICASSP.2019.8682869 | [
"Jingcai Guo",
"Song Guo"
] | In most recent years, zero-shot recognition (ZSR) has gained increasing attention in machine learning and image processing fields. It aims at recognizing unseen class instances with knowledge transferred from seen classes. This is typically achieved by exploiting a pre-defined semantic feature space (FS), i.e., semanti... | # | ICASSP2019 |
Sparse Learning of Parsimonious Reproducing Kernel Hilbert Space Models | https://doi.org/10.1109/ICASSP.2019.8682173 | [
"Maria Peifer",
"Luiz F. O. Chamon",
"Santiago Paternain",
"Alejandro Ribeiro"
] | Reproducing kernel ilbert spaces (RKHSs) have been at the core of successful non-parametric tools in signal processing, statistics, and machine learning. Despite their success, the computational complexity of these models often hinders their use in practice. Indeed, fitting RKHS models typically relies on representer t... | # | ICASSP2019 |
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