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Neural and Evolutionary Computing

Authors and titles for recent submissions

[ total of 37 entries: 1-10 | 11-20 | 21-30 | 31-37 ]
[ showing 10 entries per page: fewer | more | all ]

Mon, 29 Apr 2024

[1]  arXiv:2404.17456 [pdf, other]
Title: Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs
Authors: Zhipeng Huang, Jianhao Ding, Zhiyu Pan, Haoran Li, Ying Fang, Zhaofei Yu, Jian K. Liu
Subjects: Neural and Evolutionary Computing (cs.NE)
[2]  arXiv:2404.17323 [pdf, other]
Title: A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories
Authors: Niki van Stein, Sarah L. Thomson, Anna V. Kononova
Comments: 15 pages, 5 figures, submitted to PPSN 2024
Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
[3]  arXiv:2404.17292 [pdf, other]
Title: The Inefficiency of Genetic Programming for Symbolic Regression -- Extended Version
Authors: Gabriel Kronberger, Fabricio Olivetti de Franca, Harry Desmond, Deaglan J. Bartlett, Lukas Kammerer
Comments: This is an extended version of the article submitted to Parallel Problem Solving from Nature (PPSN) Conference 2024
Subjects: Neural and Evolutionary Computing (cs.NE); Astrophysics of Galaxies (astro-ph.GA); Instrumentation and Methods for Astrophysics (astro-ph.IM)
[4]  arXiv:2404.17241 [pdf, ps, other]
Title: Synchronized Stepwise Control of Firing and Learning Thresholds in a Spiking Randomly Connected Neural Network toward Hardware Implementation
Authors: Kumiko Nomura, Yoshifumi Nishi
Comments: 18 pages, 9 figures, 1 table
Subjects: Neural and Evolutionary Computing (cs.NE)
[5]  arXiv:2404.16906 [pdf, other]
Title: Evolve Cost-aware Acquisition Functions Using Large Language Models
Authors: Yiming Yao, Fei Liu, Ji Cheng, Qingfu Zhang
Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
[6]  arXiv:2404.17099 (cross-list from cs.LG) [pdf, other]
Title: Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
Authors: Qiyu Kang, Kai Zhao, Qinxu Ding, Feng Ji, Xuhao Li, Wenfei Liang, Yang Song, Wee Peng Tay
Comments: The Twelfth International Conference on Learning Representations
Subjects: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
[7]  arXiv:2404.17048 (cross-list from cs.ET) [pdf, other]
Title: Transductive Spiking Graph Neural Networks for Loihi
Authors: Shay Snyder (1), Victoria Clerico (1, 2), Guojing Cong (3), Shruti Kulkarni (3), Catherine Schuman (4), Sumedh R. Risbud (5), Maryam Parsa (1) ((1) George Mason University, (2) Universidad Politecnica de Madrid, (3) Oak Ridge National Laboratory, (4) University of Tennessee - Knoxville, (5) Intel Labs)
Comments: 6 pages, 4 figures, 3 tables
Subjects: Emerging Technologies (cs.ET); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
[8]  arXiv:2404.16908 (cross-list from cs.LG) [pdf, other]
Title: Closing the gap: Optimizing Guidance and Control Networks through Neural ODEs
Authors: Sebastien Origer, Dario Izzo
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)

Fri, 26 Apr 2024 (showing first 2 of 6 entries)

[9]  arXiv:2404.16280 [pdf, ps, other]
Title: An Efficient Reconstructed Differential Evolution Variant by Some of the Current State-of-the-art Strategies for Solving Single Objective Bound Constrained Problems
Authors: Sichen Tao, Ruihan Zhao, Kaiyu Wang, Shangce Gao
Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
[10]  arXiv:2404.16218 [pdf, other]
Title: Efficient NAS with FaDE on Hierarchical Spaces
Authors: Simon Neumeyer, Julian Stier, Michael Granitzer
Journal-ref: Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol 14642. Springer, Cham
Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
[ total of 37 entries: 1-10 | 11-20 | 21-30 | 31-37 ]
[ showing 10 entries per page: fewer | more | all ]

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