02.03.19 Evolutionary papers

01-30-2019	Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM by Hongxu Yin et al
01-30-2019	The Evolved Transformer by David R. So et al
01-30-2019	Code Farming: A Process for Creating Generic Computational Building Blocks by David Landaeta
02-01-2019	Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design by Jonathan Ho et al
01-31-2019	Improving Evolutionary Strategies with Generative Neural Networks by Louis Faury et al
01-29-2019	Learning Choice Functions by Karlson Pfannschmidt et al
01-29-2019	Self-Supervised Deep Image Denoising by Samuli Laine et al
01-31-2019	Network Parameter Learning Using Nonlinear Transforms, Local Representation Goals and Local Propagation Constraints by Dimche Kostadinov et al
02-01-2019	Fast Re-Optimization via Structural Diversity by Benjamin Doerr et al
01-30-2019	Neuroevolution with Perceptron Turing Machines by David Landaeta
01-30-2019	Recurrent Neural Networks for P300-based BCI by Ori Tal et al
01-30-2019	Unsupervised Scalable Representation Learning for Multivariate Time Series by Jean-Yves Franceschi et al
01-30-2019	Compositionality for Recursive Neural Networks by Martha Lewis
01-30-2019	Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution by Ke Li et al
01-31-2019	Parallel Black-Box Complexity with Tail Bounds by Per Kristian Lehre et al
01-29-2019	Minimax-optimal decoding of movement goals from local field potentials using complex spectral features by Marko Angjelichinoski et al
01-29-2019	Numerically Recovering the Critical Points of a Deep Linear Autoencoder by Charles G. Frye et al