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IEEE Transactions on Neural Networks and Learning Systems

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https://www.readbyqxmd.com/read/30640631/adversarial-examples-attacks-and-defenses-for-deep-learning
#1
Xiaoyong Yuan, Pan He, Qile Zhu, Xiaolin Li
With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks (DNNs) have been recently found vulnerable to well-designed input samples called adversarial examples. Adversarial perturbations are imperceptible to human but can easily fool DNNs in the testing/deploying stage. The vulnerability to adversarial examples becomes one of the major risks for applying DNNs in safety-critical environments...
January 14, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30640630/learning-with-annotation-of-various-degrees
#2
Joey Tianyi Zhou, Meng Fang, Hao Zhang, Chen Gong, Xi Peng, Zhiguo Cao, Rick Siow Mong Goh
In this paper, we study a new problem in the scenario of sequences labeling. To be exact, we consider that the training data are with annotation of various degrees, namely, fully labeled, unlabeled, and partially labeled sequences. The learning with fully un/labeled sequence refers to the standard setting in traditional un/supervised learning, and the proposed partially labeling specifies the subject that the element does not belong to. The partially labeled data are cheaper to obtain compared with the fully labeled data though it is less informative, especially when the tasks require a lot of domain knowledge...
January 14, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30640635/classification-by-sparse-neural-networks
#3
Vera Kurkova, Marcello Sanguineti
The choice of dictionaries of computational units suitable for efficient computation of binary classification tasks is investigated. To deal with exponentially growing sets of tasks with increasingly large domains, a probabilistic model is introduced. The relevance of tasks for a given application area is modeled by a product probability distribution on the set of all binary-valued functions. Approximate measures of network sparsity are studied in terms of variational norms tailored to dictionaries of computational units...
January 10, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30640634/eeg-based-spatio-temporal-convolutional-neural-network-for-driver-fatigue-evaluation
#4
Zhongke Gao, Xinmin Wang, Yuxuan Yang, Chaoxu Mu, Qing Cai, Weidong Dang, Siyang Zuo
Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based on the spatial-temporal structure of multichannel electroencephalogram (EEG) signals, we develop a novel EEG-based spatial-temporal convolutional neural network (ESTCNN) to detect driver fatigue. First, we introduce the core block to extract temporal dependencies from EEG signals. Then, we employ dense layers to fuse spatial features and realize classification...
January 10, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30640633/simultaneously-learning-neighborship-and-projection-matrix-for-supervised-dimensionality-reduction
#5
Yanwei Pang, Bo Zhou, Feiping Nie
Explicitly or implicitly, most dimensionality reduction methods need to determine which samples are neighbors and the similarities between the neighbors in the original high-dimensional space. The projection matrix is then learnt on the assumption that the neighborhood information, e.g., the similarities, are known and fixed prior to learning. However, it is difficult to precisely measure the intrinsic similarities of samples in high-dimensional space because of the curse of dimensionality. Consequently, the neighbors selected according to such similarities and the projection matrix obtained according to such similarities and the corresponding neighbors might not be optimal in the sense of classification and generalization...
January 10, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30640632/mining-top-k-useful-negative-sequential-patterns-via-learning
#6
Xiangjun Dong, Ping Qiu, Jinhu Lu, Longbing Cao, Tiantian Xu
As an important tool for behavior informatics, negative sequential patterns (NSPs) (such as missing a medical treatment) are sometimes much more informative than positive sequential patterns (PSPs) (e.g., attending a medical treatment) in many applications. However, NSP mining is at an early stage and faces many challenging problems, including 1) how to mine an expected number of NSPs; 2) how to select useful NSPs; and 3) how to reduce high time consumption. To solve the first problem, we propose an algorithm Topk-NSP to mine the k most frequent negative patterns...
January 10, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30629518/less-is-more-a-comprehensive-framework-for-the-number-of-components-of-ensemble-classifiers
#7
Hamed Bonab, Fazli Can
The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of the existing batch and online ensemble classifiers. There are only a limited number of studies on the ensemble size examining majority voting (MV) and weighted MV (WMV). Almost all of them are designed for batch-mode, hardly addressing online environments. Big data dimensions and resource limitations, in terms of time and memory, make the determination of ensemble size crucial, especially for online environments...
January 9, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30629517/tractable-learning-and-inference-for-large-scale-probabilistic-boolean-networks
#8
Ifigeneia Apostolopoulou, Diana Marculescu
Probabilistic Boolean networks (PBNs) have previously been proposed so as to gain insights into complex dynamical systems. However, identification of large networks and their underlying discrete Markov chain which describes their temporal evolution still remains a challenge. In this paper, we introduce an equivalent representation for PBNs, the stochastic conjunctive normal form network (SCNFN), which enables a scalable learning algorithm and helps predict long-run dynamic behavior of large-scale systems. State-of-the-art methods turn out to be 400 times slower for middle-sized networks (i...
January 9, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30629516/identification-and-control-of-nonlinear-systems-using-neural-networks-a-singularity-free-approach
#9
Dong-Dong Zheng, Yongping Pan, Kai Guo, Haoyong Yu
In this paper, identification and control for a class of nonlinear systems with unknown constant or variable control gains are investigated. By reformulating the original system dynamic equation into a new form with a unit control gain and introducing a set of filtered variables, a novel neural network (NN) estimator is constructed and a new estimation error is used to update the augmented weights. Based on the identification results, two singularity-free NN indirect adaptive controllers are developed for nonlinear systems with unknown constant control gains or variable control gains, respectively...
January 8, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30629515/task-oriented-gan-for-polsar-image-classification-and-clustering
#10
Fang Liu, Licheng Jiao, Xu Tang
Based on a generative adversarial network (GAN), a novel version named Task-Oriented GAN is proposed to tackle difficulties in PolSAR image interpretation, including PolSAR data analysis and small sample problem. Besides two typical parts in GAN, i.e., generator (G-Net) and discriminator (D-Net), there is a third part named TaskNet (T-Net) in the Task-Oriented GAN, where T-Net is employed to accomplish a certain task. Two tasks, PolSAR image classification and clustering, are studied in this paper, where T-Net acts as a Classifier and a Clusterer, respectively...
January 8, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30624233/neural-network-controller-design-for-a-class-of-nonlinear-delayed-systems-with-time-varying-full-state-constraints
#11
Dapeng Li, C L Philip Chen, Yan-Jun Liu, Shaocheng Tong
This paper proposes an adaptive neural control method for a class of nonlinear time-varying delayed systems with time-varying full-state constraints. To address the problems of the time-varying full-state constraints and time-varying delays in a unified framework, an adaptive neural control method is investigated for the first time. The problems of time delay and constraint are the main factors of limiting the system performance severely and even cause system instability. The effect of unknown time-varying delays is eliminated by using appropriate Lyapunov-Krasovskii functionals...
January 7, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30624232/regularizing-deep-neural-networks-by-enhancing-diversity-in-feature-extraction
#12
Babajide O Ayinde, Tamer Inanc, Jacek M Zurada
This paper proposes a new and efficient technique to regularize the neural network in the context of deep learning using correlations among features. Previous studies have shown that oversized deep neural network models tend to produce a lot of redundant features that are either the shifted version of one another or are very similar and show little or no variations, thus resulting in redundant filtering. We propose a way to address this problem and show that such redundancy can be avoided using regularization and adaptive feature dropout mechanism...
January 7, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30624231/a-lagrangian-relaxation-approach-for-binary-multiple-instance-classification
#13
Annabella Astorino, Antonio Fuduli, Manlio Gaudioso
In the standard classification problems, the objective is to categorize points into different classes. Multiple instance learning (MIL), instead, is aimed at classifying bags of points, each point being an instance. The main peculiarity of a MIL problem is that, in the learning phase, only the label of each bag is known whereas the labels of the instances are unknown. We discuss an instance-level learning approach for a binary MIL classification problem characterized by two classes of instances, positive and negative, respectively...
January 7, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30624230/recurrent-neural-network-model-a-new-strategy-to-solve-fuzzy-matrix-games
#14
Amin Mansoori, Mohammad Eshaghnezhad, Sohrab Effati
This paper aims to investigate the fuzzy constrained matrix game (MG) problems using the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first in attempting to find a solution for fuzzy game problems using RNN models. For this purpose, a fuzzy game problem is reformulated into a weighting problem. Then, the Karush-Kuhn-Tucker (KKT) optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Moreover, the Lyapunov stability and the global convergence of the RNN model are also confirmed...
January 7, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30624229/learning-a-low-tensor-train-rank-representation-for-hyperspectral-image-super-resolution
#15
Renwei Dian, Shutao Li, Leyuan Fang
Hyperspectral images (HSIs) with high spectral resolution only have the low spatial resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution can be obtained with higher spatial resolution. Therefore, fusing the high-spatial-resolution MSI (HR-MSI) with low-spatial-resolution HSI of the same scene has become the very popular HSI super-resolution scheme. In this paper, a novel low tensor-train (TT) rank (LTTR)-based HSI super-resolution method is proposed, where an LTTR prior is designed to learn the correlations among the spatial, spectral, and nonlocal modes of the nonlocal similar high-spatial-resolution HSI (HR-HSI) cubes...
January 7, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30624228/submodular-function-optimization-for-motion-clustering-and-image-segmentation
#16
Jianbing Shen, Xingping Dong, Jianteng Peng, Xiaogang Jin, Ling Shao, Fatih Porikli
In this paper, we propose a framework of maximizing quadratic submodular energy with a knapsack constraint approximately, to solve certain computer vision problems. The proposed submodular maximization problem can be viewed as a generalization of the classic 0/1 knapsack problem. Importantly, maximization of our knapsack constrained submodular energy function can be solved via dynamic programing. We further introduce a range-reduction step prior to dynamic programing as a two-stage procedure for more efficient maximization...
January 7, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30624227/a-multistage-game-in-smart-grid-security-a-reinforcement-learning-solution
#17
Zhen Ni, Shuva Paul
Existing smart grid security research investigates different attack techniques and cascading failures from the attackers' viewpoints, while the defenders' or the operators' protection strategies are somehow neglected. Game theoretic methods are applied for the attacker-defender games in the smart grid security area. Yet, most of the existing works only use the one-shot game and do not consider the dynamic process of the electric power grid. In this paper, we propose a new solution for a multistage game (also called a dynamic game) between the attacker and the defender based on reinforcement learning to identify the optimal attack sequences given certain objectives (e...
January 7, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30605109/plume-tracing-via-model-free-reinforcement-learning-method
#18
Hangkai Hu, Shiji Song, C L Phillip Chen
This paper studies the plume-tracing strategy for an autonomous underwater vehicle (AUV) in the deep-sea turbulent environment. The tracing problem is modeled as a partially observable Markov decision process with continuous state space and action space due to the spatio-temporal changes of environment. An long short-term memory-based reinforcement learning framework with full use of history information is proposed to generate a smooth strategy while the AUV interacting with the environment. Continuous temporal difference and deterministic policy gradient methods are employed to improve the strategy...
January 1, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30605108/a-novel-equivalent-model-of-active-distribution-networks-based-on-lstm
#19
Chao Zheng, Shaorong Wang, Yilu Liu, Chengxi Liu, Wei Xie, Chen Fang, Shu Liu
Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention...
January 1, 2019: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30605107/extended-dissipativity-analysis-for-markovian-jump-neural-networks-with-time-varying-delay-via-delay-product-type-functionals
#20
Wen-Juan Lin, Yong He, Chuan-Ke Zhang, Min Wu, Jianhua Shen
This paper investigates the problem of extended dissipativity for Markovian jump neural networks (MJNNs) with a time-varying delay. The objective is to derive less conservative extended dissipativity criteria for delayed MJNNs. Toward this aim, an appropriate Lyapunov-Krasovskii functional (LKF) with some improved delay-product-type terms is first constructed. Then, by employing the extended reciprocally convex matrix inequality (ERCMI) and the Wirtinger-based integral inequality to estimate the derivative of the constructed LKF, a delay-dependent extended dissipativity condition is derived for the delayed MJNNs...
January 1, 2019: IEEE Transactions on Neural Networks and Learning Systems
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