# IEEE Transactions on Neural Networks and Learning Systems

Includes the top 50 most frequently accessed documents for this publication according to the usage statistics for the month of

• ### Efficient kNN Classification With Different Numbers of Nearest Neighbors

Publication Year: 2018, Page(s):1774 - 1785
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k nearest neighbor (kNN) method is a popular classification method in data mining and statistics because of its simple implementation and significant classification performance. However, it is impractical for traditional kNN methods to assign a fixed k value (even though set by experts) to all test samples. Previous solutions assign different k values to different test samples by the cross validat... View full abstract»

• ### Optimal and Autonomous Control Using Reinforcement Learning: A Survey

Publication Year: 2018, Page(s):2042 - 2062
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This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal H2 and H∞ control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and ... View full abstract»

• ### Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

Publication Year: 2018, Page(s):10 - 24
Cited by:  Papers (3)
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Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is establis... View full abstract»

• ### Applications of Deep Learning and Reinforcement Learning to Biological Data

Publication Year: 2018, Page(s):2063 - 2079
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Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent re... View full abstract»

• ### LSTM: A Search Space Odyssey

Publication Year: 2017, Page(s):2222 - 2232
Cited by:  Papers (12)
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Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants. In t... View full abstract»

• ### Extreme Learning Machine for Multilayer Perceptron

Publication Year: 2016, Page(s):809 - 821
Cited by:  Papers (105)
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Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number... View full abstract»

• ### Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

Publication Year: 2017, Page(s):653 - 664
Cited by:  Papers (2)
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Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automa... View full abstract»

• ### Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene

Publication Year: 2017, Page(s):690 - 703
Cited by:  Papers (7)
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Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for succe... View full abstract»

• ### Evaluating the Visualization of What a Deep Neural Network Has Learned

Publication Year: 2017, Page(s):2660 - 2673
Cited by:  Papers (3)
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Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision, given a new unseen data sample. Recently, several approaches... View full abstract»

• ### Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming

Publication Year: 2016, Page(s):2351 - 2363
Cited by:  Papers (2)
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A wash trade refers to the illegal activities of traders who utilize carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, a wash trade can be extremely damaging to the proper functioning and integrity of capital markets. The existing work focuses on collusive clique detections ba... View full abstract»

• ### Action-Driven Visual Object Tracking With Deep Reinforcement Learning

Publication Year: 2018, Page(s):2239 - 2252
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In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and... View full abstract»

• ### Structure Learning for Deep Neural Networks Based on Multiobjective Optimization

Publication Year: 2018, Page(s):2450 - 2463
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This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible... View full abstract»

• ### A Survey of Memristive Threshold Logic Circuits

Publication Year: 2017, Page(s):1734 - 1746
Cited by:  Papers (1)
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In this paper, we review different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of the flow of neurotransmitters in the biological brain. The brainlike generalization ability and the area minimization of these threshold logic circuits aim toward crossing Moore's law boundaries at device, circuits, and systems levels. Fast switching memory, signal processing,... View full abstract»

• ### Classification in the Presence of Label Noise: A Survey

Publication Year: 2014, Page(s):845 - 869
Cited by:  Papers (118)
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Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the ... View full abstract»

• ### Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

Publication Year: 2016, Page(s):125 - 138
Cited by:  Papers (30)
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This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. The approach accomplishes the detection of the changed and unchanged areas by designing a deep neural network. The main guideline is to produce a change detection map directly from two images with the trained deep neural network. The method can omit the process of generating a differen... View full abstract»

• ### Convolution in Convolution for Network in Network

Publication Year: 2018, Page(s):1587 - 1597
Cited by:  Papers (1)
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Network in network (NiN) is an effective instance and an important extension of deep convolutional neural network consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow multilayer perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and 1 × 1 convolutions in... View full abstract»

• ### Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning

Publication Year: 2018, Page(s):2192 - 2203
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Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning a... View full abstract»

• ### Online Learning Algorithm Based on Adaptive Control Theory

Publication Year: 2018, Page(s):2278 - 2293
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This paper proposes a new online learning algorithm which is based on adaptive control (AC) theory, thus, we call this proposed algorithm as AC algorithm. Comparing to the gradient descent (GD) and exponential gradient (EG) algorithm which have been applied to online prediction problems, we find a new form of AC theory for online prediction problems and investigate two key questions: how to get a ... View full abstract»

• ### A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control

Publication Year: 2016, Page(s):416 - 425
Cited by:  Papers (230)
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This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period Td is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the... View full abstract»

• ### Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

Publication Year: 2018, Page(s):1174 - 1186
Cited by:  Papers (6)
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This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there... View full abstract»

• ### Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems

Publication Year: 2018, Page(s):1033 - 1045
Cited by:  Papers (3)
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This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN... View full abstract»

• ### DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks

Publication Year: 2018, Page(s):1441 - 1453
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Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limi... View full abstract»

• ### Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

Publication Year: 2017, Page(s):2371 - 2381
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Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network a... View full abstract»

• ### Convolutional Sparse Autoencoders for Image Classification

Publication Year: 2018, Page(s):3289 - 3294
Cited by:  Papers (1)
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Convolutional sparse coding (CSC) can model local connections between image content and reduce the code redundancy when compared with patch-based sparse coding. However, CSC needs a complicated optimization procedure to infer the codes (i.e., feature maps). In this brief, we proposed a convolutional sparse auto-encoder (CSAE), which leverages the structure of the convolutional AE and incorporates ... View full abstract»

• ### A Cost-Sensitive Deep Belief Network for Imbalanced Classification

Publication Year: 2018, Page(s):1 - 14
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Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassi... View full abstract»

• ### Dynamic Energy Management System for a Smart Microgrid

Publication Year: 2016, Page(s):1643 - 1656
Cited by:  Papers (14)
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This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are s... View full abstract»

• ### Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks

Publication Year: 2018, Page(s):681 - 694
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In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the perform... View full abstract»

• ### Optimal Guaranteed Cost Sliding Mode Control for Constrained-Input Nonlinear Systems With Matched and Unmatched Disturbances

Publication Year: 2018, Page(s):2112 - 2126
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Based on integral sliding mode and approximate dynamic programming (ADP) theory, a novel optimal guaranteed cost sliding mode control is designed for constrained-input nonlinear systems with matched and unmatched disturbances. When the system moves on the sliding surface, the optimal guaranteed cost control problem of sliding mode dynamics is transformed into the optimal control problem of a refor... View full abstract»

• ### On Deep Learning for Trust-Aware Recommendations in Social Networks

Publication Year: 2017, Page(s):1164 - 1177
Cited by:  Papers (2)
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With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommend... View full abstract»

• ### Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization

Publication Year: 2018, Page(s):1 - 13
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Inspired by the behavior of humans talking in noisy environments, we propose an embodied embedded cognition approach to improve automatic speech recognition (ASR) systems for robots in challenging environments, such as with ego noise, using binaural sound source localization (SSL). The approach is verified by measuring the impact of SSL with a humanoid robot head on the performance of an ASR syste... View full abstract»

• ### Robust Latent Subspace Learning for Image Classification

Publication Year: 2018, Page(s):2502 - 2515
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This paper proposes a novel method, called robust latent subspace learning (RLSL), for image classification. We formulate an RLSL problem as a joint optimization problem over both the latent SL and classification model parameter predication, which simultaneously minimizes: 1) the regression loss between the learned data representation and objective outputs and 2) the reconstruction error between t... View full abstract»

• ### Leader–Follower Output Synchronization of Linear Heterogeneous Systems With Active Leader Using Reinforcement Learning

Publication Year: 2018, Page(s):2139 - 2153
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This paper develops optimal control protocols for the distributed output synchronization problem of leader-follower multiagent systems with an active leader. Agents are assumed to be heterogeneous with different dynamics and dimensions. The desired trajectory is assumed to be preplanned and is generated by the leader. Other follower agents autonomously synchronize to the leader by interacting with... View full abstract»

• ### A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine

Publication Year: 2018, Page(s):2337 - 2351
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As data sets become larger and more complicated, an extreme learning machine (ELM) that runs in a traditional serial environment cannot realize its ability to be fast and effective. Although a parallel ELM (PELM) based on MapReduce to process large-scale data shows more efficient learning speed than identical ELM algorithms in a serial environment, some operations, such as intermediate results sto... View full abstract»

• ### Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure

Publication Year: 2018, Page(s):2099 - 2111
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Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only stru... View full abstract»

• ### Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

Publication Year: 2018, Page(s):1301 - 1313
Cited by:  Papers (7)
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In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual qu... View full abstract»

• ### A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images

Publication Year: 2018, Page(s):545 - 559
Cited by:  Papers (3)
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We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. Th... View full abstract»

• ### Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems

Publication Year: 2018, Page(s):2614 - 2624
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This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally di... View full abstract»

• ### Distributed Optimal Consensus Over Resource Allocation Network and Its Application to Dynamical Economic Dispatch

Publication Year: 2018, Page(s):2407 - 2418
Cited by:  Papers (1)
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The resource allocation problem is studied and reformulated by a distributed interior point method via a θ-logarithmic barrier. By the facilitation of the graph Laplacian, a fully distributed continuous-time multiagent system is developed for solving the problem. Specifically, to avoid high singularity of the θ-logarithmic barrier at boundary, an adaptive parameter switching strategy... View full abstract»

• ### Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning

Publication Year: 2018, Page(s):2731 - 2742
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In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. S... View full abstract»

• ### Guided Policy Exploration for Markov Decision Processes Using an Uncertainty-Based Value-of-Information Criterion

Publication Year: 2018, Page(s):2080 - 2098
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Reinforcement learning in environments with many action-state pairs is challenging. The issue is the number of episodes needed to thoroughly search the policy space. Most conventional heuristics address this search problem in a stochastic manner. This can leave large portions of the policy space unvisited during the early training stages. In this paper, we propose an uncertainty-based, information... View full abstract»

• ### Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning

Publication Year: 2018, Page(s):2259 - 2270
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The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions' consequences may facilitate such knowledge transfer. We consider here domains where an RL agent has access... View full abstract»

• ### Optimal Synchronization Control of Multiagent Systems With Input Saturation via Off-Policy Reinforcement Learning

Publication Year: 2018, Page(s):1 - 12
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In this paper, we aim to investigate the optimal synchronization problem for a group of generic linear systems with input saturation. To seek the optimal controller, Hamilton-Jacobi-Bellman (HJB) equations involving nonquadratic input energy terms in coupled forms are established. The solutions to these coupled HJB equations are further proven to be optimal and the induced controllers constitute i... View full abstract»

• ### Driving Under the Influence (of Language)

Publication Year: 2018, Page(s):2668 - 2683
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We present a unified framework which supports grounding natural-language semantics in robotic driving. This framework supports acquisition (learning grounded meanings of nouns and prepositions from human sentential annotation of robotic driving paths), generation (using such acquired meanings to generate sentential description of new robotic driving paths), and comprehension (using such acquired m... View full abstract»

• ### Graph Regularized Restricted Boltzmann Machine

Publication Year: 2018, Page(s):2651 - 2659
| | PDF (874 KB) | HTML Media

The restricted Boltzmann machine (RBM) has received an increasing amount of interest in recent years. It determines good mapping weights that capture useful latent features in an unsupervised manner. The RBM and its generalizations have been successfully applied to a variety of image classification and speech recognition tasks. However, most of the existing RBM-based models disregard the preservat... View full abstract»

• ### Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks

Publication Year: 2018, Page(s):1 - 6
| | PDF (1146 KB)

Convolutional neural networks (CNNs) have led to remarkable progress in a number of key pattern recognition tasks, such as visual scene understanding and speech recognition, that potentially enable numerous applications. Consequently, there is a significant need to deploy trained CNNs to resource-constrained embedded systems. Inference using pretrained modern deep CNNs, however, requires significa... View full abstract»

• ### Blind Denoising Autoencoder

Publication Year: 2018, Page(s):1 - 6
| | PDF (1334 KB)

The term blind denoising' refers to the fact that the basis used for denoising is learned from the noisy sample itself during denoising. Dictionary learning- and transform learning-based formulations for blind denoising are well known. But there has been no autoencoder-based solution for the said blind denoising approach. So far, autoencoder-based denoising formulations have learned the model on... View full abstract»

• ### A Discrete-Time Recurrent Neural Network for Solving Rank-Deficient Matrix Equations With an Application to Output Regulation of Linear Systems

Publication Year: 2018, Page(s):2271 - 2277
Cited by:  Papers (1)
| | PDF (954 KB) | HTML

This paper presents a discrete-time recurrent neural network approach to solving systems of linear equations with two features. First, the system of linear equations may not have a unique solution. Second, the system matrix is not known precisely, but a sequence of matrices that converges to the unknown system matrix exponentially is known. The problem is motivated from solving the output regulati... View full abstract»

• ### GoDec+: Fast and Robust Low-Rank Matrix Decomposition Based on Maximum Correntropy

Publication Year: 2018, Page(s):2323 - 2336
Cited by:  Papers (1)
| | PDF (4026 KB) | HTML

GoDec is an efficient low-rank matrix decomposition algorithm. However, optimal performance depends on sparse errors and Gaussian noise. This paper aims to address the problem that a matrix is composed of a low-rank component and unknown corruptions. We introduce a robust local similarity measure called correntropy to describe the corruptions and, in doing so, obtain a more robust and faster low-r... View full abstract»

• ### Online Hashing

Publication Year: 2018, Page(s):2309 - 2322
Cited by:  Papers (2)
| | PDF (1579 KB) | HTML

Although hash function learning algorithms have achieved great success in recent years, most existing hash models are off-line, which are not suitable for processing sequential or online data. To address this problem, this paper proposes an online hash model to accommodate data coming in stream for online learning. Specifically, a new loss function is proposed to measure the similarity loss betwee... View full abstract»

• ### Improved Stability and Stabilization Results for Stochastic Synchronization of Continuous-Time Semi-Markovian Jump Neural Networks With Time-Varying Delay

Publication Year: 2018, Page(s):2488 - 2501
| | PDF (2023 KB) | HTML

Continuous-time semi-Markovian jump neural networks (semi-MJNNs) are those MJNNs whose transition rates are not constant but depend on the random sojourn time. Addressing stochastic synchronization of semi-MJNNs with time-varying delay, an improved stochastic stability criterion is derived in this paper to guarantee stochastic synchronization of the response systems with the drive systems. This is... View full abstract»

## Aims & Scope

IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Haibo He
Dept. of Electrical, Computer, and Biomedical Engineering
University of Rhode Island
Kingston, RI 02881, USA
ieeetnnls@gmail.com