2021.6.21 Neural papers

06-16-2021	Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components by Unai Garciarena et al
06-18-2021	A Fresh Approach to Evaluate Performance in Distributed Parallel Genetic Algorithms by Tomohiro Harada et al
06-18-2021	World-GAN: a Generative Model for Minecraft Worlds by Maren Awiszus et al
06-18-2021	Meta-control of social learning strategies by Anil Yaman et al
06-15-2021	Causal Navigation by Continuous-time Neural Networks by Charles Vorbach et al
06-16-2021	Evolving Image Compositions for Feature Representation Learning by Paola Cascante-Bonilla et al
06-16-2021	A Spiking Neural Network for Image Segmentation by Kinjal Patel et al
06-17-2021	A Simple Generative Network by Daniel N. Nissani
06-17-2021	Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay by Yoojin Choi et al
06-17-2021	Voice2Series: Reprogramming Acoustic Models for Time Series Classification by Chao-Han Huck Yang et al
06-17-2021	Effective Model Sparsification by Scheduled Grow-and-Prune Methods by Xiaolong Ma et al
06-17-2021	Pruning Randomly Initialized Neural Networks with Iterative Randomization by Daiki Chijiwa et al
06-16-2021	RHNAS: Realizable Hardware and Neural Architecture Search by Yash Akhauri et al
06-16-2021	Improving Inference Lifetime of Neuromorphic Systems via Intelligent Synapse Mapping by Shihao Song et al
06-17-2021	Orthogonal-Pad\e Activation Functions: Trainable Activation functions for smooth and faster convergence in deep networks by Koushik Biswas et al
06-16-2021	Selecting for Selection: Learning To Balance Adaptive and Diversifying Pressures in Evolutionary Search by Kevin Frans et al