2021.10.4 Neural papers

09-29-2021	Industrial Application of Artificial Intelligence to the Traveling Salesperson Problem by Michael Lehenauer et al
09-28-2021	Customs Fraud Detection in the Presence of Concept Drift by Tung-Duong Mai et al
10-01-2021	Guiding Evolutionary Strategies by Differentiable Robot Simulators by Vladislav Kurenkov et al
09-28-2021	StereoSpike: Depth Learning with a Spiking Neural Network by Ulysse Rançon et al
09-28-2021	Extensible Logging and Empirical Attainment Function for IOHexperimenter by Johann Dreo et al
09-30-2021	Variational learning of quantum ground states on spiking neuromorphic hardware by Robert Klassert et al
09-28-2021	Faster Improvement Rate Population Based Training by Valentin Dalibard et al
09-29-2021	Stability Analysis of Fractional Order Memristor Synapse-coupled Hopfield Neural Network with Ring Structure by Leila Eftekhari et al
10-01-2021	New Evolutionary Computation Models and their Applications to Machine Learning by Mihai Oltean
09-29-2021	Double framed moduli spaces of quiver representations by Marco Armenta et al
09-30-2021	Compositional generalization in semantic parsing with pretrained transformers by A. Emin Orhan
09-29-2021	On Assessing the Usefulness of Proxy Domains for Developing and Evaluating Embodied Agents by Anthony Courchesne et al
10-01-2021	Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework by Zhuowen Zou et al
09-29-2021	Influence of the Binomial Crossover on Performance of Randomized Search Heuristics by Cong Wang et al
09-29-2021	Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State by Mingqing Xiao et al
09-30-2021	A Novel Simplified Swarm Optimization for Generalized Reliability Redundancy Allocation Problem by Zhenyao Liu et al
09-28-2021	Neural Dependency Coding inspired Multimodal Fusion by Shiv Shankar
09-28-2021	Multi Expression Programming -- an in-depth description by Mihai Oltean
09-30-2021	Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks by Mufeng Tang et al
09-29-2021	A Comprehensive Survey and Performance Analysis of Activation Functions in Deep Learning by Shiv Ram Dubey et al