2022.5.23 Neural papers

05-17-2022	Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift by Kingson Man et al
05-20-2022	Nothing makes sense in deep learning, except in the light of evolution by Artem Kaznatcheev et al
05-18-2022	Deep Features for CBIR with Scarce Data using Hebbian Learning by Gabriele Lagani et al
05-19-2022	Focused Adversarial Attacks by Thomas Cilloni et al
05-18-2022	Large Neural Networks Learning from Scratch with Very Few Data and without Regularization by Christoph Linse et al
05-20-2022	The Unreasonable Effectiveness of Deep Evidential Regression by Nis Meinert et al
05-17-2022	Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data by Manar Samad et al
05-20-2022	EXODUS: Stable and Efficient Training of Spiking Neural Networks by Felix Christian Bauer et al
05-18-2022	Relational representation learning with spike trains by Dominik Dold
05-17-2022	Variable length genetic algorithm with continuous parameters optimization of beam layout in proton therapy by François Smekens et al
05-19-2022	Design and Mathematical Modelling of Inter Spike Interval of Temporal Neuromorphic Encoder for Image Recognition by Aadhitiya VS et al
05-19-2022	Analyzing Echo-state Networks Using Fractal Dimension by Norbert Michael Mayer et al
05-20-2022	Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism by Haokai Hong et al
05-17-2022	Function Regression using Spiking DeepONet by Adar Kahana et al
05-17-2022	Towards the optimization of ballistics in proton therapy using genetic algorithms: implementation issues by François Smekens et al
05-19-2022	Spikemax: Spike-based Loss Methods for Classification by Sumit Bam Shrestha et al