2019.08.04 Neural papers

07-31-2019	Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview by Jiawei Zhang
07-31-2019	Evolutionary Dataset Optimisation: learning algorithm quality through evolution by Henry Wilde et al
07-31-2019	Neural networks-based backward scheme for fully nonlinear PDEs by Huyen Pham et al
07-31-2019	On the difficulty of learning and predicting the long-term dynamics of bouncing objects by Alberto Cenzato et al
08-01-2019	LoadCNN: A Efficient Green Deep Learning Model for Day-ahead Individual Resident Load Forecasting by Yunyou Huang et al
08-01-2019	Conditional Finite Mixtures of Poisson Distributions for Context-Dependent Neural Correlations by Sacha Sokoloski et al
08-01-2019	A self-organizing fuzzy neural network for sequence learning by Armin Salimi-Badr et al
08-01-2019	Multi-Scale Learned Iterative Reconstruction by Andreas Hauptmann et al
08-01-2019	Estimation of Tire-Road Friction for Autonomous Vehicles: a Neural Network Approach by Alexandre M. Ribeiro et al
08-02-2019	Merging variables: one technique of search in pseudo-Boolean optimization by Alexander A. Semenov
07-31-2019	Competitive Co-evolution for Dynamic Constrained Optimisation by Xiaofen Lu et al
08-01-2019	Popt4jlib: A Parallel/Distributed Optimization Library for Java by Ioannis T. Christou