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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961 to 1,020 of 1,365

Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias Khalil +3
cs.LGcs.DScs.NEarXiv:2102.09544v32021
Guiding Deep Learning System Testing using Surprise Adequacy
Jinhan Kim, Robert Feldt, Shin Yoo
cs.SEcs.NEarXiv:1808.08444v12018
Hello Edge: Keyword Spotting on Microcontrollers
Yundong Zhang, Naveen Suda, Liangzhen Lai +1
cs.SDcs.CLcs.LGarXiv:1711.07128v32017
Homo-RAG: Homology-Guided Retrieval-Augmented Generation for Cross-Species Gene Function Prediction
Azrin Sultana
cs.NEcs.AIarXiv:2608.25466v12026
Sensitivity and Generalization in Neural Networks: an Empirical Study
Roman Novak, Yasaman Bahri, Daniel A. Abolafia +2
stat.MLcs.AIcs.LGarXiv:1802.08760v32018
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Jonathan Ho, Xi Chen, Aravind Srinivas +2
cs.LGcs.NEstat.MLarXiv:1902.00275v22019
Modular Multitask Reinforcement Learning with Policy Sketches
Jacob Andreas, Dan Klein, Sergey Levine
cs.LGcs.NEarXiv:1611.01796v22016
Learning Activation Functions to Improve Deep Neural Networks
Forest Agostinelli, Matthew Hoffman, Peter Sadowski +1
cs.NEcs.CVcs.LGarXiv:1412.6830v32014
Planning to Explore via Self-Supervised World Models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis +3
cs.LGcs.AIcs.CVarXiv:2005.05960v22020
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
Chen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu
stat.MLcs.LGcs.NEarXiv:1509.08985v22015
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
Zhun Fan, Wenji Li, Xinye Cai +5
cs.NEcs.AIarXiv:1709.05915v12017
Correlation Alignment for Unsupervised Domain Adaptation
Baochen Sun, Jiashi Feng, Kate Saenko
cs.CVcs.AIcs.NEarXiv:1612.01939v12016
Synthesis of Hopfield Neural Network: Novel Results
Garimella Rama Murthy
cs.NEarXiv:2608.25481v12026
Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates
Leslie N. Smith, Nicholay Topin
cs.LGcs.CVcs.NEarXiv:1708.07120v32017
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
Tianfu Li, Zhibin Zhao, Chuang Sun +4
cs.CVcs.LGcs.NEarXiv:1911.07925v32019
Learning to Decode Linear Codes Using Deep Learning
Eliya Nachmani, Yair Beery, David Burshtein
cs.ITcs.LGcs.NEarXiv:1607.04793v22016
Towards Explainable Artificial Intelligence
Wojciech Samek, Klaus-Robert Müller
cs.AIcs.LGcs.NEarXiv:1909.12072v12019
Dense Associative Memory for Pattern Recognition
Dmitry Krotov, John J Hopfield
cs.NEcond-mat.dis-nncs.LGarXiv:1606.01164v22016
Real-time closed-loop protocol to assess neural variability in temporal coding
Alberto Ayala, Angel Lareo, Pablo Varona +1
q-bio.NCcs.AIcs.NEarXiv:2608.24895v12026
Response Renormalization for Critical Deep Equilibrium Models
Jose Luis Lima de Jesus Silva
cs.LGcs.NEarXiv:2608.23725v12026
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
Grace W. Lindsay
q-bio.NCcs.CVcs.NEarXiv:2001.07092v22020
Pointing the Unknown Words
Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati +2
cs.CLcs.LGcs.NEarXiv:1603.08148v32016
Massively Parallel Methods for Deep Reinforcement Learning
Arun Nair, Praveen Srinivasan, Sam Blackwell +11
cs.LGcs.AIcs.DCarXiv:1507.04296v22015
Learning Features by Watching Objects Move
Deepak Pathak, Ross Girshick, Piotr Dollár +2
cs.CVcs.AIcs.LGarXiv:1612.06370v22016
A survey on modern trainable activation functions
Andrea Apicella, Francesco Donnarumma, Francesco Isgrò +1
cs.LGcs.NEstat.MLarXiv:2005.00817v42020
Convolutional Recurrent Neural Networks for Music Classification
Keunwoo Choi, George Fazekas, Mark Sandler +1
cs.NEcs.LGcs.MMarXiv:1609.04243v32016
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
Martin Engelcke, Dushyant Rao, Dominic Zeng Wang +2
cs.ROcs.AIcs.CVarXiv:1609.06666v22016
Summaries:한국어
Reverse Curriculum Generation for Reinforcement Learning
Carlos Florensa, David Held, Markus Wulfmeier +2
cs.AIcs.LGcs.NEarXiv:1707.05300v32017
Spikformer: When Spiking Neural Network Meets Transformer
Zhaokun Zhou, Yuesheng Zhu, Chao He +4
cs.NEcs.CVcs.LGarXiv:2209.15425v22022
Predictive Business Process Monitoring with LSTM Neural Networks
Niek Tax, Ilya Verenich, Marcello La Rosa +1
stat.APcs.DBcs.LGarXiv:1612.02130v22016
A Clockwork RNN
Jan Koutník, Klaus Greff, Faustino Gomez +1
cs.NEcs.LGarXiv:1402.3511v12014
Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research
Varun Kumar Ojha, Ajith Abraham, Václav Snášel
cs.NEcs.LGarXiv:1705.05584v12017
Learning to Generate Reviews and Discovering Sentiment
Alec Radford, Rafal Jozefowicz, Ilya Sutskever
cs.LGcs.CLcs.NEarXiv:1704.01444v22017
Recurrent Neural Network Grammars
Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros +1
cs.CLcs.NEarXiv:1602.07776v42016
Submanifold Sparse Convolutional Networks
Benjamin Graham, Laurens van der Maaten
cs.NEcs.CVarXiv:1706.01307v12017
Deep Learning Methods for Improved Decoding of Linear Codes
Eliya Nachmani, Elad Marciano, Loren Lugosch +3
cs.ITcs.NEarXiv:1706.07043v22017
Learning Explanatory Rules from Noisy Data
Richard Evans, Edward Grefenstette
cs.NEmath.LOarXiv:1711.04574v22017
Convolutional Neural Networks Applied to House Numbers Digit Classification
Pierre Sermanet, Soumith Chintala, Yann LeCun
cs.CVcs.LGcs.NEarXiv:1204.3968v12012
Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski +2
cs.CLcs.AIcs.LGarXiv:2309.16797v12023
Demystifying Neural Style Transfer
Yanghao Li, Naiyan Wang, Jiaying Liu +1
cs.CVcs.LGcs.NEarXiv:1701.01036v22017
NiftyNet: a deep-learning platform for medical imaging
Eli Gibson, Wenqi Li, Carole Sudre +14
cs.CVcs.LGcs.NEarXiv:1709.03485v22017
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks
Hardik Sharma, Jongse Park, Naveen Suda +5
cs.NEcs.ARarXiv:1712.01507v22017
Insights on representational similarity in neural networks with canonical correlation
Ari S. Morcos, Maithra Raghu, Samy Bengio
stat.MLcs.AIcs.CVarXiv:1806.05759v32018
Learning to See by Moving
Pulkit Agrawal, Joao Carreira, Jitendra Malik
cs.CVcs.NEcs.ROarXiv:1505.01596v22015
Globally Normalized Transition-Based Neural Networks
Daniel Andor, Chris Alberti, David Weiss +5
cs.CLcs.LGcs.NEarXiv:1603.06042v22016
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Xin Dong, Shangyu Chen, Sinno Jialin Pan
cs.NEcs.CVcs.LGarXiv:1705.07565v22017
Building Energy Load Forecasting using Deep Neural Networks
Daniel L. Marino, Kasun Amarasinghe, Milos Manic
cs.NEarXiv:1610.09460v12016
Qualitatively characterizing neural network optimization problems
Ian J. Goodfellow, Oriol Vinyals, Andrew M. Saxe
cs.NEcs.LGstat.MLarXiv:1412.6544v62014
Deep API Learning
Xiaodong Gu, Hongyu Zhang, Dongmei Zhang +1
cs.SEcs.CLcs.LGarXiv:1605.08535v32016
Measuring the Intrinsic Dimension of Objective Landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu +1
cs.LGcs.NEstat.MLarXiv:1804.08838v12018
Learning to Compose Neural Networks for Question Answering
Jacob Andreas, Marcus Rohrbach, Trevor Darrell +1
cs.CLcs.CVcs.NEarXiv:1601.01705v42016
Binary Neural Networks: A Survey
Haotong Qin, Ruihao Gong, Xianglong Liu +3
cs.NEcs.CVcs.LGarXiv:2004.03333v12020
A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations
Holger R. Roth, Le Lu, Ari Seff +6
cs.CVcs.LGcs.NEarXiv:1406.2639v12014
Classifying Relations by Ranking with Convolutional Neural Networks
Cicero Nogueira dos Santos, Bing Xiang, Bowen Zhou
cs.CLcs.LGcs.NEarXiv:1504.06580v22015
Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization
Xin-She Yang, M. Karamanoglu, X. S. He
math.OCcs.NEnlin.AOarXiv:1408.5332v12014
Mode Regularized Generative Adversarial Networks
Tong Che, Yanran Li, Athul Paul Jacob +2
cs.LGcs.AIcs.CVarXiv:1612.02136v52016
A Survey on Evolutionary Neural Architecture Search
Yuqiao Liu, Yanan Sun, Bing Xue +3
cs.NEarXiv:2008.10937v42020
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
Izhar Wallach, Michael Dzamba, Abraham Heifets
cs.LGcs.NEq-bio.BMarXiv:1510.02855v12015
Deep Learning for Classification of Hyperspectral Data: A Comparative Review
Nicolas Audebert, Bertrand Saux, Sébastien Lefèvre
cs.LGcs.CVcs.NEarXiv:1904.10674v12019
Learning Continuous Control Policies by Stochastic Value Gradients
Nicolas Heess, Greg Wayne, David Silver +3
cs.LGcs.NEarXiv:1510.09142v12015

Neural and Evolutionary Computing

Combinatorial optimization and reasoning with graph neural networks

Guiding Deep Learning System Testing using Surprise Adequacy

Hello Edge: Keyword Spotting on Microcontrollers

Homo-RAG: Homology-Guided Retrieval-Augmented Generation for Cross-Species Gene Function Prediction

Sensitivity and Generalization in Neural Networks: an Empirical Study

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Modular Multitask Reinforcement Learning with Policy Sketches

Learning Activation Functions to Improve Deep Neural Networks

Planning to Explore via Self-Supervised World Models

Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree

Push and Pull Search for Solving Constrained Multi-objective Optimization Problems

Correlation Alignment for Unsupervised Domain Adaptation

Synthesis of Hopfield Neural Network: Novel Results

Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates

WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

Learning to Decode Linear Codes Using Deep Learning

Towards Explainable Artificial Intelligence

Dense Associative Memory for Pattern Recognition

Real-time closed-loop protocol to assess neural variability in temporal coding

Response Renormalization for Critical Deep Equilibrium Models

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

Pointing the Unknown Words

Massively Parallel Methods for Deep Reinforcement Learning

Learning Features by Watching Objects Move

A survey on modern trainable activation functions

Convolutional Recurrent Neural Networks for Music Classification

Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

Reverse Curriculum Generation for Reinforcement Learning

Spikformer: When Spiking Neural Network Meets Transformer

Predictive Business Process Monitoring with LSTM Neural Networks

A Clockwork RNN

Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research

Learning to Generate Reviews and Discovering Sentiment

Recurrent Neural Network Grammars

Submanifold Sparse Convolutional Networks

Deep Learning Methods for Improved Decoding of Linear Codes

Learning Explanatory Rules from Noisy Data

Convolutional Neural Networks Applied to House Numbers Digit Classification

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Demystifying Neural Style Transfer

NiftyNet: a deep-learning platform for medical imaging

Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks

Insights on representational similarity in neural networks with canonical correlation

Learning to See by Moving

Globally Normalized Transition-Based Neural Networks

Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon

Building Energy Load Forecasting using Deep Neural Networks

Qualitatively characterizing neural network optimization problems

Deep API Learning

Measuring the Intrinsic Dimension of Objective Landscapes

Learning to Compose Neural Networks for Question Answering

Binary Neural Networks: A Survey

A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

Classifying Relations by Ranking with Convolutional Neural Networks

Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization

Mode Regularized Generative Adversarial Networks

A Survey on Evolutionary Neural Architecture Search

AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

Deep Learning for Classification of Hyperspectral Data: A Comparative Review

Learning Continuous Control Policies by Stochastic Value Gradients