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🏛️ The Neural & Evolutionary Crypt
cs.NE: Where Neural & Evolutionary papers rest without their code.
6370
Total Papers
5604
No Code
153
Twilight
613
Has Code
9.6%
Survival Rate
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Hardware-Friendly Implementation of Physical Reservoir Computing with CMOS-based Time-domain Analog Spiking Neurons
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Cross-Country Comparative Analysis of Climate Resilience and Localized Mapping in Data-Sparse Regions
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Training Spiking Neural Networks via Augmented Direct Feedback Alignment
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A Comprehensive Comparison Between ANNs and KANs For Classifying EEG Alzheimer's Data
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How noise affects memory in linear recurrent networks
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Maelstrom Networks
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On-Chip Learning with Memristor-Based Neural Networks: Assessing Accuracy and Efficiency Under Device Variations, Conductance Errors, and Input Noise
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Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
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Massive Dimensions Reduction and Hybridization with Meta-heuristics in Deep Learning
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ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics
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Multi-objective Memetic Algorithm with Adaptive Weights for Inverse Antenna Design
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Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models
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SHA-CNN: Scalable Hierarchical Aware Convolutional Neural Network for Edge AI
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Neuromorphic on-chip reservoir computing with spiking neural network architectures
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A Crowding Distance That Provably Solves the Difficulties of the NSGA-II in Many-Objective Optimization
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Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm
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Voltage-Controlled Magnetoelectric Devices for Neuromorphic Diffusion Process
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Deep Learning-Based Operators for Evolutionary Algorithms
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Evolved Developmental Artificial Neural Networks for Multitasking with Advanced Activity Dependence
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Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap Function
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Neuromimetic metaplasticity for adaptive continual learning
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Decomposition of Difficulties in Complex Optimization Problems Using a Bilevel Approach
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