Neural and Evolutionary Computing

Papers filed under cs.NE on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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361 to 420 of 1,362

Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation
Iulia M. Comsa, Krzysztof Potempa, Luca Versari +3
cs.NEcs.LGq-bio.NCarXiv:1907.13223v32019
VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
Arash Ardakani, François Leduc-Primeau, Naoya Onizawa +2
cs.NEcs.ARarXiv:1509.08972v22015
Associative Long Short-Term Memory
Ivo Danihelka, Greg Wayne, Benigno Uria +2
cs.NEarXiv:1602.03032v22016
All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation
Di Xie, Jiang Xiong, Shiliang Pu
cs.CVcs.LGcs.NEarXiv:1703.01827v32017
Artificial Neural Networks Applied to Taxi Destination Prediction
Alexandre de Brébisson, Étienne Simon, Alex Auvolat +2
cs.LGcs.NEarXiv:1508.00021v22015
Random vector functional link network: recent developments, applications, and future directions
A. K. Malik, Ruobin Gao, M. A. Ganaie +2
cs.NEcs.LGcs.ROarXiv:2203.11316v22022
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Andy Brown, Aaron Tuor, Brian Hutchinson +1
cs.LGcs.NEstat.MLarXiv:1803.04967v12018
On the Power and Limitations of Random Features for Understanding Neural Networks
Gilad Yehudai, Ohad Shamir
cs.LGcs.NEstat.MLarXiv:1904.00687v42019
Network Morphism
Tao Wei, Changhu Wang, Yong Rui +1
cs.LGcs.CVcs.NEarXiv:1603.01670v22016
Mathematical exploration and discovery at scale
Bogdan Georgiev, Javier Gómez-Serrano, Terence Tao +1
cs.NEcs.AImath.CAarXiv:2511.02864v32025
H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression
Anchit Mulye, Rhythm Baghel, Sujay Kumar Ingle +1
cs.LGcs.ARcs.NEarXiv:2609.02684v12026
DL-Droid: Deep learning based android malware detection using real devices
Mohammed K. Alzaylaee, Suleiman Y. Yerima, Sakir Sezer
cs.CRcs.LGcs.NEarXiv:1911.10113v12019
Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
John R. Hershey, Jonathan Le Roux, Felix Weninger
cs.LGcs.NEstat.MLarXiv:1409.2574v42014
Do Deep Nets Really Need to be Deep?
Lei Jimmy Ba, Rich Caruana
cs.LGcs.NEarXiv:1312.6184v72013
A Study of NK Landscapes' Basins and Local Optima Networks
Gabriela Ochoa, Marco Tomassini, Sébastien Verel +1
cs.NEarXiv:0810.3484v12008
Discovering Hidden Factors of Variation in Deep Networks
Brian Cheung, Jesse A. Livezey, Arjun K. Bansal +1
cs.LGcs.CVcs.NEarXiv:1412.6583v42014
Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang +2
cs.LGcs.AIcs.CVarXiv:1807.09341v12018
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger +2
cs.LGcs.NEstat.MLarXiv:1708.08012v32017
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
Franck Dernoncourt, Ji Young Lee, Peter Szolovits
cs.CLcs.NEstat.MLarXiv:1705.05487v12017
Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline
Ole Richter, Yannan Xing, Michele De Marchi +9
cs.NEcs.LGeess.IVarXiv:2304.06793v22023
The Deterministic Dendritic Cell Algorithm
Julie Greensmith, Uwe Aickelin
cs.AIcs.NEarXiv:1006.1512v12010
Elucidating the Design Space of Diffusion-Based Generative Models
Tero Karras, Miika Aittala, Timo Aila +1
cs.CVcs.AIcs.LGarXiv:2206.00364v22022
Exploring the Space of Adversarial Images
Pedro Tabacof, Eduardo Valle
cs.NEarXiv:1510.05328v52015
Understanding the Role of Training Regimes in Continual Learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu +1
cs.LGcs.NEstat.MLarXiv:2006.06958v12020
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry +2
cs.NEcs.LGarXiv:1609.07061v12016
Multiclass Linear Perceptrons with Multiplicative Margins
Dmitri Rachkovskij, Evgeny Osipov, Olexander Volkov +2
cs.LGcs.NEarXiv:2608.30028v12026
Procedural Content Metageneration via Program Search and Continual Abstraction Discovery
Matthew Siper, Ahmed Khalifa, Julian Togelius
cs.AIcs.LGcs.NEarXiv:2608.17947v12026
Summaries:한국어
Blockwisely Supervised Neural Architecture Search with Knowledge Distillation
Changlin Li, Jiefeng Peng, Liuchun Yuan +4
cs.CVcs.LGcs.NEarXiv:1911.13053v22019
CEM-RL: Combining evolutionary and gradient-based methods for policy search
Aloïs Pourchot, Olivier Sigaud
cs.LGcs.NEstat.MLarXiv:1810.01222v32018
A Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization
Víctor Gallego
cs.LGcs.NEarXiv:2608.08156v12026
Unified Neural Scaling Laws
Ethan Caballero, Priyank Jaini, David Krueger +1
cs.LGcs.AIcs.NEarXiv:2605.26248v12026
Denoising Diffusion Generative Models Secretly Calculate Attentions
Farzan Haddadi, Leila Monfared, Ebrahim Rezaii +3
cs.AIcs.CVcs.LGarXiv:2609.00885v12026
Deep Multiagent Reinforcement Learning: Challenges and Directions
Annie Wong, Thomas Bäck, Anna V. Kononova +1
cs.LGcs.AIcs.MAarXiv:2106.15691v22021
A Review of Evolutionary Multi-modal Multi-objective Optimization
Ryoji Tanabe, Hisao Ishibuchi
cs.NEarXiv:2009.13347v12020
AlphaStar: An Evolutionary Computation Perspective
Kai Arulkumaran, Antoine Cully, Julian Togelius
cs.NEcs.AIcs.LGarXiv:1902.01724v32019
Multimodal similarity-preserving hashing
Jonathan Masci, Michael M. Bronstein, Alexander A. Bronstein +1
cs.CVcs.NEarXiv:1207.1522v12012
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Kaifeng Lyu, Jian Li
cs.LGcs.NEstat.MLarXiv:1906.05890v42019
Improved Precision and Recall Metric for Assessing Generative Models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine +2
stat.MLcs.LGcs.NEarXiv:1904.06991v32019
FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review
Ahmad Shawahna, Sadiq M. Sait, Aiman El-Maleh
cs.NEcs.ARcs.CVarXiv:1901.00121v12019
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, Clément Hongler
cs.LGcs.NEmath.PRarXiv:1806.07572v42018
Continuous Learning in Single-Incremental-Task Scenarios
Davide Maltoni, Vincenzo Lomonaco
cs.LGcs.AIcs.CVarXiv:1806.08568v32018
Supervised Speech Separation Based on Deep Learning: An Overview
DeLiang Wang, Jitong Chen
cs.CLcs.LGcs.NEarXiv:1708.07524v22017
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
Weinan E, Jiequn Han, Arnulf Jentzen
math.NAcs.LGcs.NEarXiv:1706.04702v12017
A Survey of Neuromorphic Computing and Neural Networks in Hardware
Catherine D. Schuman, Thomas E. Potok, Robert M. Patton +4
cs.NEarXiv:1705.06963v12017
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
Ye Tian, Ran Cheng, Xingyi Zhang +1
cs.NEarXiv:1701.00879v12017
Memory-Efficient Backpropagation Through Time
Audrūnas Gruslys, Remi Munos, Ivo Danihelka +2
cs.NEcs.LGarXiv:1606.03401v12016
Graying the black box: Understanding DQNs
Tom Zahavy, Nir Ben Zrihem, Shie Mannor
cs.LGcs.AIcs.NEarXiv:1602.02658v42016
Model-Based Active Exploration
Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez
cs.LGcs.AIcs.ITarXiv:1810.12162v52018
Deep Quaternion Networks
Chase Gaudet, Anthony Maida
cs.NEcs.CVarXiv:1712.04604v32017
Tensorizing Neural Networks
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin +1
cs.LGcs.NEarXiv:1509.06569v22015
Learning to Execute
Wojciech Zaremba, Ilya Sutskever
cs.NEcs.AIcs.LGarXiv:1410.4615v32014
Emergent Models: Intelligence from Tiny Substrates
Giacomo Bocchese, Nicola Giacobbo, Etienne Guichard +2
cs.NEcs.LGarXiv:2608.14019v12026
Continual Evolution Strategies in Control Tasks
Nicola Pitzalis, Eleni Nisioti, Antonio Carta +2
cs.NEcs.LGarXiv:2608.13600v12026
A swarm optimization algorithm inspired in the behavior of the social-spider
Erik Cuevas, Miguel Cienfuegos, Daniel Zaldivar +1
cs.NEarXiv:1406.3282v12014
optimize_anything: A Universal API for Optimizing any Text Parameter
Lakshya A Agrawal, Donghyun Lee, Shangyin Tan +11
cs.CLcs.AIcs.LGarXiv:2605.19633v12026
Understanding the Role of Individual Units in a Deep Neural Network
David Bau, Jun-Yan Zhu, Hendrik Strobelt +3
cs.CVcs.LGcs.NEarXiv:2009.05041v22020
The evolutionary origins of modularity
Jeff Clune, Jean-Baptiste Mouret, Hod Lipson
q-bio.PEcs.NEq-bio.MNarXiv:1207.2743v22012
Differentiable Surface Splatting for Point-based Geometry Processing
Wang Yifan, Felice Serena, Shihao Wu +2
cs.GRcs.NEarXiv:1906.04173v32019
On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora, Simon S. Du, Wei Hu +3
cs.LGcs.CVcs.NEarXiv:1904.11955v22019
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
Man Yao, Jiakui Hu, Tianxiang Hu +5
cs.NEcs.CVarXiv:2404.03663v12024

Neural and Evolutionary Computing

Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation

VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing

Associative Long Short-Term Memory

All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation

Artificial Neural Networks Applied to Taxi Destination Prediction

Random vector functional link network: recent developments, applications, and future directions

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

On the Power and Limitations of Random Features for Understanding Neural Networks

Network Morphism

Mathematical exploration and discovery at scale

H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression

DL-Droid: Deep learning based android malware detection using real devices

Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

Do Deep Nets Really Need to be Deep?

A Study of NK Landscapes' Basins and Local Optima Networks

Discovering Hidden Factors of Variation in Deep Networks

Learning Plannable Representations with Causal InfoGAN

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

NeuroNER: an easy-to-use program for named-entity recognition based on neural networks

Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline

The Deterministic Dendritic Cell Algorithm

Elucidating the Design Space of Diffusion-Based Generative Models

Exploring the Space of Adversarial Images

Understanding the Role of Training Regimes in Continual Learning

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Multiclass Linear Perceptrons with Multiplicative Margins

Procedural Content Metageneration via Program Search and Continual Abstraction Discovery

Blockwisely Supervised Neural Architecture Search with Knowledge Distillation

CEM-RL: Combining evolutionary and gradient-based methods for policy search

A Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization

Unified Neural Scaling Laws

Denoising Diffusion Generative Models Secretly Calculate Attentions

Deep Multiagent Reinforcement Learning: Challenges and Directions

A Review of Evolutionary Multi-modal Multi-objective Optimization

AlphaStar: An Evolutionary Computation Perspective

Multimodal similarity-preserving hashing

Gradient Descent Maximizes the Margin of Homogeneous Neural Networks

Improved Precision and Recall Metric for Assessing Generative Models

FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review

Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Continuous Learning in Single-Incremental-Task Scenarios

Supervised Speech Separation Based on Deep Learning: An Overview

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

A Survey of Neuromorphic Computing and Neural Networks in Hardware

PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization

Memory-Efficient Backpropagation Through Time

Graying the black box: Understanding DQNs

Model-Based Active Exploration

Deep Quaternion Networks

Tensorizing Neural Networks

Learning to Execute

Emergent Models: Intelligence from Tiny Substrates

Continual Evolution Strategies in Control Tasks

A swarm optimization algorithm inspired in the behavior of the social-spider

optimize_anything: A Universal API for Optimizing any Text Parameter

Understanding the Role of Individual Units in a Deep Neural Network

The evolutionary origins of modularity

Differentiable Surface Splatting for Point-based Geometry Processing

On Exact Computation with an Infinitely Wide Neural Net

Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips