Suchergebnisse - "Computer Science - Neural and Evolutionary Computing"

A third-order finite difference weighted essentially non-oscillatory scheme with shallow neural network

Autoren: Kwanghyuk Park Xinjuan Chen Dongjin Lee et al.

Quelle: Applied Numerical Mathematics. 218:1-21

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Mathematics, Computer Science - Neural and Evolutionary Computing, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2407.06333

Automatic Implementation of Neural Networks Through Reaction Networks—Part I: Circuit Design and Convergence Analysis

Autoren: Yuzhen Fan Xiaoyu Zhang Chuanhou Gao et al.

Quelle: IEEE Transactions on Automatic Control. 70:6356-6371

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Mathematics, Computer Science - Neural and Evolutionary Computing, Dynamical Systems (math.DS), Neural and Evolutionary Computing (cs.NE), Mathematics - Dynamical Systems, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2311.18313

AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms With Diverse Structures

Autoren: Qi Zhao Bai Yan Taiwei Hu et al.

Quelle: IEEE Transactions on Emerging Topics in Computational Intelligence. 9:3690-3703

Schlagwörter: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Computer Science - Neural and Evolutionary Computing, 02 engineering and technology, Neural and Evolutionary Computing (cs.NE)

Zugangs-URL: http://arxiv.org/abs/2204.00998

Physics Informed Neural Network—Estimated Circuit Parameter Adaptive Modulation of DAB

Autoren: Saikat Dey Ayan Mallik

Quelle: IEEE Transactions on Power Electronics. 40:14821-14841

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Systems and Control (eess.SY), Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control

Zugangs-URL: http://arxiv.org/abs/2502.17452

LightSNN: Lightweight Architecture Search for Sparse and Accurate Spiking Neural Networks

Autoren: Abdennadher, Yesmine Perin, Giovanni Mazzieri, Riccardo et al.

Quelle: IEEE International Conference on Advanced Machine Learning and Data Science (AMLDS)

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Electrical Engineering and Systems Science - Signal Processing

Zugangs-URL: http://arxiv.org/abs/2503.21846

Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression

Autoren: Luigi Rovito Marco Virgolin Rovito, Luigi et al.

Weitere Verfasser: Luigi Rovito Marco Virgolin Rovito, Luigi et al.

Quelle: Proceedings of the Genetic and Evolutionary Computation Conference. :453-462

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Symbolic Regression, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2504.05756
https://hdl.handle.net/11368/3113341

Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy, Stochastic or Uncertain Domains

Autoren: Flageat, Manon Huber, Johann Helenon, François et al.

Weitere Verfasser: Flageat, Manon Huber, Johann Helenon, François et al.

Quelle: Proceedings of the Genetic and Evolutionary Computation Conference. :140-148

Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE)

Zugangs-URL: http://arxiv.org/abs/2502.06585
https://hal.science/hal-05313280v1
https://doi.org/10.1145/3712256.3726404

Sequential Learning in the Dense Associative Memory

Autoren: McAlister, Hayden Robins, Anthony Szymanski, Lech

Quelle: Neural Computation. 37:1877-1924

Schlagwörter: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE)

Zugangs-URL: http://arxiv.org/abs/2409.15729

Neuromorphic Circuit for Temporal Odor Encoding in Turbulent Environments

Autoren: Shavika Rastogi Nik Dennler Michael Schmuker et al.

Quelle: IEEE Sensors Journal. 25:35622-35630

Schlagwörter: FOS: Computer and information sciences, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE)

Zugangs-URL: http://arxiv.org/abs/2412.20117

Strong Anti-Hebbian Plasticity Alters the Convexity of Network Attractor Landscapes

Autoren: Lulu Gong Xudong Chen ShiNung Ching

Quelle: IEEE Transactions on Neural Networks and Learning Systems. 36:17491-17498

Schlagwörter: FOS: Computer and information sciences, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Computer Science - Neural and Evolutionary Computing, Neurons and Cognition (q-bio.NC), Neural and Evolutionary Computing (cs.NE), Systems and Control (eess.SY), Dynamical Systems (math.DS), Mathematics - Dynamical Systems, Electrical Engineering and Systems Science - Systems and Control

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40305254
http://arxiv.org/abs/2312.14896

Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering

Autoren: Yiqun Zhang Sen Feng Pengkai Wang et al.

Quelle: IEEE Transactions on Neural Networks and Learning Systems. 36:16049-16061

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG)

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40392647
http://arxiv.org/abs/2404.09243

Converting High-Performance and Low-Latency SNNs Through Explicit Modeling of Residual Error in ANNs

Autoren: Zhipeng Huang Jianhao Ding Zhiyu Pan et al.

Quelle: IEEE Transactions on Neural Networks and Learning Systems. 36:16788-16802

Schlagwörter: FOS: Computer and information sciences, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE)

Zugangs-URL: http://arxiv.org/abs/2404.17456

A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers

Autoren: Diederick Vermetten Catalin-Viorel Dinu Marcus Gallagher

Quelle: Proceedings of the 18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. :297-307

Schlagwörter: FOS: Computer and information sciences, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE)

Zugangs-URL: http://arxiv.org/abs/2505.09233

Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-Based Function Concatenations

Autoren: Michal Prusik Bartosz Frej Michal W. Przewozniczek

Quelle: Proceedings of the 18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. :226-237

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Mathematics - Optimization and Control, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2503.17397

Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language Models

Autoren: Xuan Wu Di Wang Chunguo Wu et al.

Quelle: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. :3228-3239

Schlagwörter: FOS: Computer and information sciences, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE)

Zugangs-URL: http://arxiv.org/abs/2505.12627

A Survey on Distributed Evolutionary Computation

Autoren: Feng-Feng Wei Wei-Neng Chen Tian-Fang Zhao et al.

Quelle: IEEE Computational Intelligence Magazine. 20:41-62

Schlagwörter: FOS: Computer and information sciences, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Distributed, Parallel, and Cluster Computing (cs.DC)

Zugangs-URL: http://arxiv.org/abs/2304.05811

Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression

Autoren: Nathan Haut Wolfgang Banzhaf Bill Punch

Quelle: IEEE Transactions on Evolutionary Computation. 29:1100-1111

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2308.00672

Real-Time Progressive Learning: Accumulate Knowledge From Control With Neural-Network-Based Selective Memory

Autoren: Yiming Fei Jiangang Li Yanan Li

Quelle: IEEE Transactions on Neural Networks and Learning Systems. 36:14464-14478

Schlagwörter: FOS: Computer and information sciences, 93-10, FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Neural and Evolutionary Computing, Systems and Control (eess.SY), Neural and Evolutionary Computing (cs.NE), Electrical Engineering and Systems Science - Systems and Control

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40030759
http://arxiv.org/abs/2308.04223

Exploratory Landscape Analysis for Mixed-Variable Problems

Autoren: Raphael Patrick Prager Heike Trautmann

Quelle: IEEE Transactions on Evolutionary Computation. 29:936-947

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, Computer Science - Neural and Evolutionary Computing, 02 engineering and technology

Zugangs-URL: http://arxiv.org/abs/2402.16467

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach

Autoren: M. Tanveer Anushka Tiwari Mushir Akhtar et al.

Quelle: IEEE Transactions on Emerging Topics in Computational Intelligence. 9:3112-3121

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2411.17128