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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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Self-Evolutionary Reservoir Computer Based on Kuramoto Model
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Learning to Control Rapidly Changing Synaptic Connections: An Alternative Type of Memory in Sequence Processing Artificial Neural Networks
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Universal Time-Uniform Trajectory Approximation for Random Dynamical Systems with Recurrent Neural Networks
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Curiosity creates Diversity in Policy Search
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On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks
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A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks
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Biological connectomes as a representation for the architecture of artificial neural networks
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On the benefits of self-taught learning for brain decoding
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Evolving Complexity is Hard
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An Automated News Bias Classifier Using Caenorhabditis Elegans Inspired Recursive Feedback Network Architecture
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Evolutionary Strategies for the Design of Binary Linear Codes
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Surrogate-assisted level-based learning evolutionary search for heat extraction optimization of enhanced geothermal system
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Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
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Constraints on the design of neuromorphic circuits set by the properties of neural population codes
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Adaptive Scenario Subset Selection for Worst-Case Optimization and its Application to Well Placement Optimization
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The GA-cal software for the automatic calibration of soil constitutive laws: a tutorial and a user manual
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Introducing topography in convolutional neural networks
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Hierarchical Automatic Power Plane Generation with Genetic Optimization and Multilayer Perceptron
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Adaptive scaling of the learning rate by second order automatic differentiation
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Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems
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Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms
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A Step Towards Uncovering The Structure of Multistable Neural Networks
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