IEEE Transactions on Neural Networks and Learning Systems
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Year: 2023 | Volume: 34 | Issue: 5
IEEE Transactions on Neural Networks and Learning Systems Publication Information
Publication Year: 2023,Page(s):C2 - C2
IEEE Transactions on Neural Networks and Learning Systems Publication Information
Year: 2023 | Volume: 34 | Issue: 5
Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review
Publication Year: 2023,Page(s):2170 - 2190
Cited by: Papers (23)
Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents’ behavioral patterns. With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents’ behavioral rules into an adaptive model. Even thou...Show More
Variable Binding for Sparse Distributed Representations: Theory and Applications
Publication Year: 2023,Page(s):2191 - 2204
Cited by: Papers (27)
Variable binding is a cornerstone of symbolic reasoning and cognition. But how binding can be implemented in connectionist models has puzzled neuroscientists, cognitive psychologists, and neural network researchers for many decades. One type of connectionist model that naturally includes a binding operation is vector symbolic architectures (VSAs). In contrast to other proposals for variable bindin...Show More
Kronecker CP Decomposition With Fast Multiplication for Compressing RNNs
Dingheng Wang;Bijiao Wu;Guangshe Zhao;Man Yao;Hengnu Chen;Lei Deng;Tianyi Yan;Guoqi Li
Publication Year: 2023,Page(s):2205 - 2219
Cited by: Papers (11)
Recurrent neural networks (RNNs) are powerful in the tasks oriented to sequential data, such as natural language processing and video recognition. However, because the modern RNNs have complex topologies and expensive space/computation complexity, compressing them becomes a hot and promising topic in recent years. Among plenty of compression methods, tensor decomposition, e.g., tensor train (TT), ...Show More
Leaderless and Leader-Following Bipartite Consensus of Multiagent Systems With Sampled and Delayed Information
Publication Year: 2023,Page(s):2220 - 2233
Cited by: Papers (20)
This article proposes a hybrid systems approach to address the sampled-data leaderless and leader-following bipartite consensus problems of multiagent systems (MAS) with communication delays. First, distributed asynchronous sampled-data bipartite consensus protocols are proposed based on estimators. Then, by introducing appropriate intermediate variables and internal auxiliary variables, a unified...Show More
Nonlinear Causal Discovery for High-Dimensional Deterministic Data
Yan Zeng;Zhifeng Hao;Ruichu Cai;Feng Xie;Libo Huang;Shohei Shimizu
Publication Year: 2023,Page(s):2234 - 2245
Cited by: Papers (5)
Nonlinear causal discovery with high-dimensional data where each variable is multidimensional plays a significant role in many scientific disciplines, such as social network analysis. Previous work majorly focuses on exploiting asymmetry in the causal and anticausal directions between two high-dimensional variables (a cause–effect pair). Although there exist some works that concentrate on the caus...Show More
Bidirectional Relationship Inferring Network for Referring Image Localization and Segmentation
Publication Year: 2023,Page(s):2246 - 2258
Cited by: Papers (11)
Recently, referring image localization and segmentation has aroused widespread interest. However, the existing methods lack a clear description of the interdependence between language and vision. To this end, we present a bidirectional relationship inferring network (BRINet) to effectively address the challenging tasks. Specifically, we first employ a vision-guided linguistic attention module to p...Show More
Orthogonal Inductive Matrix Completion
Publication Year: 2023,Page(s):2259 - 2270
Cited by: Papers (4)
We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all comp...Show More
Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks
Cheng Yang;Hao Wang;Jian Tang;Chuan Shi;Maosong Sun;Ganqu Cui;Zhiyuan Liu
Publication Year: 2023,Page(s):2271 - 2283
Cited by: Papers (19)
Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next...Show More
Adaptive Subspace Optimization Ensemble Method for High-Dimensional Imbalanced Data Classification
Publication Year: 2023,Page(s):2284 - 2297
Cited by: Papers (17)
It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which the performance of classifiers is seriously affected and becomes poor. Although many approaches, such as resampling, cost-sensitive, and ensemble learning methods, have been proposed to deal with the skewed data, they are constrained by high-dimensional data with noise and redundancy. In this study, we pro...Show More
Impulsive-Based Almost Surely Synchronization for Neural Network Systems Subject to Deception Attacks
Publication Year: 2023,Page(s):2298 - 2307
Cited by: Papers (19)
This article is dedicated to investigating the impulsive-based almost surely synchronization issue of neural network systems (NSSs) with quality-of-service constraints. First, the communication network considered suffers from random double deception attacks, which are modeled as a nonlinear function and a desynchronizing impulse sequence, respectively. Meanwhile, the impulsive instants and impulsi...Show More
MGML: Multigranularity Multilevel Feature Ensemble Network for Remote Sensing Scene Classification
Publication Year: 2023,Page(s):2308 - 2322
Cited by: Papers (29)
Remote sensing (RS) scene classification is a challenging task to predict scene categories of RS images. RS images have two main issues: large intraclass variance caused by large resolution variance and confusing information from large geographic covering area. To ease the negative influence from the above two issues. We propose a multigranularity multilevel feature ensemble network (MGML-FENet) t...Show More
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
Publication Year: 2023,Page(s):2323 - 2337
Cited by: Papers (12)
Molecular optimization, which transforms a given input molecule $X$ into another $Y$ with desired properties, is essential in molecular drug discovery. The traditional approaches either suffer from sample-inefficient learning or ignore information that can be captured with the supervised learning of optimized molecule pairs. In this study, we present a novel molecular optimization paradigm, Gr...Show More
Surrogate-Assisted Hybrid-Model Estimation of Distribution Algorithm for Mixed-Variable Hyperparameters Optimization in Convolutional Neural Networks
Publication Year: 2023,Page(s):2338 - 2352
Cited by: Papers (42)
The performance of a convolutional neural network (CNN) heavily depends on its hyperparameters. However, finding a suitable hyperparameters configuration is difficult, challenging, and computationally expensive due to three issues, which are 1) the mixed-variable problem of different types of hyperparameters; 2) the large-scale search space of finding optimal hyperparameters; and 3) the expensive ...Show More
A Minimax Probability Machine for Nondecomposable Performance Measures
Publication Year: 2023,Page(s):2353 - 2365
Cited by: Papers (3)
Imbalanced classification tasks are widespread in many real-world applications. For such classification tasks, in comparison with the accuracy rate (AR), it is usually much more appropriate to use nondecomposable performance measures such as the area under the receiver operating characteristic curve (AUC) and the $F_\beta $ measure as the classification criterion since the label class is imbalan...Show More
Neuromorphic System Using Memcapacitors and Autonomous Local Learning
Mutsumi Kimura;Yuma Ishisaki;Yuta Miyabe;Homare Yoshida;Isato Ogawa;Tomoharu Yokoyama;Ken-Ichi Haga;Eisuke Tokumitsu;Yasuhiko Nakashima
Publication Year: 2023,Page(s):2366 - 2373
Cited by: Papers (10)
Artificial intelligence is used for various applications and is promising as an indispensable infrastructure in future societies. Neural networks are representative technologies that imitate human brains and exhibit various advantages. However, the size is bulky, the power is huge, and some advantages are not demonstrated because they are executed on Neumann-type computers. Neuromorphic systems ar...Show More
Rational Continuous Neural Network Identifier for Singular Perturbed Systems With Uncertain Dynamical Models
O. Andrianova;A. Poznyak;R. Q. Fuentes-Aguilar;Isaac Chairez
Publication Year: 2023,Page(s):2374 - 2385
Cited by: Papers (2)
This study aims at designing a robust nonparametric identifier for a class of singular perturbed systems (SPSs) with uncertain mathematical models. The identifier structure uses a novel identifier based on a differential neural network (DNN) with rational form, which can take into account the multirate nature of SPS. The identifier uses a mixed learning law including a rational formulation of neur...Show More
Inverse Reinforcement Q-Learning Through Expert Imitation for Discrete-Time Systems
Wenqian Xue;Bosen Lian;Jialu Fan;Patrik Kolaric;Tianyou Chai;Frank L. Lewis
Publication Year: 2023,Page(s):2386 - 2399
Cited by: Papers (34)
In inverse reinforcement learning (RL), there are two agents. An expert target agent has a performance cost function and exhibits control and state behaviors to a learner. The learner agent does not know the expert’s performance cost function but seeks to reconstruct it by observing the expert’s behaviors and tries to imitate these behaviors optimally by its own response. In this article, we formu...Show More
Long-Term Influenza Outbreak Forecast Using Time-Precedence Correlation of Web Data
Publication Year: 2023,Page(s):2400 - 2412
Cited by: Papers (1)
Influenza leads to many deaths every year and is a threat to human health. For effective prevention, traditional national-scale statistical surveillance systems have been developed, and numerous studies have been conducted to predict influenza outbreaks using web data. Most studies have captured the short-term signs of influenza outbreaks, such as one-week prediction using the characteristics of w...Show More
A Segmented Variable-Parameter ZNN for Dynamic Quadratic Minimization With Improved Convergence and Robustness
Lin Xiao;Yongjun He;Yaonan Wang;Jianhua Dai;Ran Wang;Wensheng Tang
Publication Year: 2023,Page(s):2413 - 2424
Cited by: Papers (13)
As a category of the recurrent neural network (RNN), zeroing neural network (ZNN) can effectively handle time-variant optimization issues. Compared with the fixed-parameter ZNN that needs to be adjusted frequently to achieve good performance, the conventional variable-parameter ZNN (VPZNN) does not require frequent adjustment, but its variable parameter will tend to infinity as time grows. Besides...Show More
Hierarchical Regression and Classification for Accurate Object Detection
Publication Year: 2023,Page(s):2425 - 2439
Cited by: Papers (6)
Accurate object detection requires correct classification and high-quality localization. Currently, most of the single shot detectors (SSDs) conduct simultaneous classification and regression using a fully convolutional network. Despite high efficiency, this structure has some inappropriate designs for accurate object detection. The first one is the mismatch of bounding box classification, where t...Show More
Modeling and Control of Robotic Manipulators Based on Symbolic Regression
Publication Year: 2023,Page(s):2440 - 2450
Cited by: Papers (7)
Model-based design is an important method of addressing problems associated with designing complex control systems. For complex dynamic systems in the presence of uncertainties, the modeling process from the first principles becomes extremely tedious and simplification in mechanism and parameter measurement may result in model inaccuracy. On the contrary, machine learning has the characteristic of...Show More
Robust to Rank Selection: Low-Rank Sparse Tensor-Ring Completion
Publication Year: 2023,Page(s):2451 - 2465
Cited by: Papers (11)
Tensor-ring (TR) decomposition was recently studied and applied for low-rank tensor completion due to its powerful representation ability of high-order tensors. However, most of the existing TR-based methods tend to suffer from deterioration when the selected rank is larger than the true one. To address this issue, this article proposes a new low-rank sparse TR completion method by imposing the Fr...Show More
Robust Discriminant Subspace Clustering With Adaptive Local Structure Embedding
Publication Year: 2023,Page(s):2466 - 2479
Cited by: Papers (2)
Unsupervised dimension reduction and clustering are frequently used as two separate steps to conduct clustering tasks in subspace. However, the two-step clustering methods may not necessarily reflect the cluster structure in the subspace. In addition, the existing subspace clustering methods do not consider the relationship between the low-dimensional representation and local structure in the inpu...Show More
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