2022.3.7 Neural papers

03-02-2022	Deep Q-network using reservoir computing with multi-layered readout by Toshitaka Matsuki
03-03-2022	Rethinking the role of normalization and residual blocks for spiking neural networks by Shin-ichi Ikegawa et al
03-04-2022	The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights by Maxime Gasse et al
03-03-2022	WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor by Ke Lin et al
03-01-2022	On genetic programming representations and fitness functions for interpretable dimensionality reduction by Thomas Uriot et al
03-03-2022	Kernel Density Estimation by Genetic Algorithm by Kiheiji Nishida
03-02-2022	Supervised Hebbian learning: toward eXplainable AI by Francesco Alemanno et al
03-02-2022	Learning in Sparse Rewards settings through Quality-Diversity algorithms by Giuseppe Paolo
03-02-2022	Rethinking Pretraining as a Bridge from ANNs to SNNs by Yihan Lin et al
03-02-2022	SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks by Peng Zhou et al
03-04-2022	HV-Net: Hypervolume Approximation based on DeepSets by Ke Shang et al
03-02-2022	A Fully Memristive Spiking Neural Network with Unsupervised Learning by Peng Zhou et al
03-01-2022	A hardware-software co-design approach to minimize the use of memory resources in multi-core neuromorphic processors by Vanessa R. C. Leite et al
03-04-2022	Improving Ant Colony Optimization Efficiency for Solving Large TSP Instances by Rafał Skinderowicz
03-02-2022	ES-dRNN with Dynamic Attention for Short-Term Load Forecasting by Slawek Smyl et al
03-03-2022	Neural Architecture Search using Progressive Evolution by Nilotpal Sinha et al
03-01-2022	An Instance Space Analysis of Constrained Multi-Objective Optimization Problems by Hanan Alsouly et al
03-02-2022	Integer Factorization with Compositional Distributed Representations by Denis Kleyko et al