Machine learning for combinatorial optimization: A methodological tour d’horizon

TL;DR: In this article , the authors proposed an upper bounding technique based on the solution of a classical (and simpler) time independent Asymmetric Travelling Salesman Problem, where the constant arc costs are suitably defined by the combined use of a linear program and a mix of unsupervised and supervised machine learning techniques.

TL;DR: In this article , a deep learning approach is proposed to find the Nash equilibrium in a chance-constrained game (CCG), which is capable of efficiently solving multiple instances of CCG in a one-shot manner.

TL;DR: In this article, the authors introduce the foundation of reinforcement learning and review some recent approaches in applying reinforcement learning to physical synthesis, and they hope to inspire more work and to see more talented ideas in this field.

TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.

TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.

TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.

TL;DR: Graph Attention Networks (GATs) as mentioned in this paper leverage masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.