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🏛️ The Neural & Evolutionary Crypt
cs.NE: Where Neural & Evolutionary papers rest without their code.
6354
Total Papers
5604
No Code
153
Twilight
597
Has Code
9.4%
Survival Rate
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Spiking Neural Streaming Binary Arithmetic
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Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block
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POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with Integer Quadratic Integrate-and-Fire Neurons
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Language Model-Based Paired Variational Autoencoders for Robotic Language Learning
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Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language Models
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Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms
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Goat Optimization Algorithm: A Novel Bio-Inspired Metaheuristic for Global Optimization
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PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing
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TACOS: Task Agnostic Continual Learning in Spiking Neural Networks
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Event-based Optical Flow on Neuromorphic Processor: ANN vs. SNN Comparison based on Activation Sparsification
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The Cartographer
Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey
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A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms
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DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware
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A reinforcement learning guided hybrid evolutionary algorithm for the latency location routing problem
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Explaining Genetic Programming Trees using Large Language Models
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JCLEC-MO: a Java suite for solving many-objective optimization engineering problems
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EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization
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Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey
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Sparse-firing regularization methods for spiking neural networks with time-to-first spike coding
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Bio-realistic Neural Network Implementation on Loihi 2 with Izhikevich Neurons
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Long Short-term Memory with Two-Compartment Spiking Neuron
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Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
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