In this review article we discuss connections between the physics of disordered systems,
phase transitions in inference problems, and computational hardness. We introduce two …

We present NeuroSAT, a message passing neural network that learns to solve SAT
problems after only being trained as a classifier to predict satisfiability. Although it is not …

This paper proposes a new optimization algorithm called Entropy-SGD for training deep
neural networks that is motivated by the local geometry of the energy landscape. Local …

This book provides an introduction to the mathematical and algorithmic foundations of data
science, including machine learning, high-dimensional geometry, and analysis of large …

The formalism of probabilistic graphical models provides a unifying framework for capturing
complex dependencies among random variables, and building large-scale multivariate …

Abstract Satisfiability Modulo Theories (SMT) refers to the problem of determining whether a
first-order formula is satisfiable with respect to some logical theory. Solvers based on SMT …

GPUs have been used to accelerate many regular applications and, more recently, irregular
applications in which the control flow and memory access patterns are data-dependent and …

A decision procedure is an algorithm that, given a decision problem, terminates with a
correct yes/no answer. In this book, we focus on decision procedures for decidable first …

Parameterized computation theory has developed rapidly over the last two decades. In
theoretical computer science, it has attracted considerable attention for its theoretical value …

In artificial neural networks, learning from data is a computationally demanding task in which
a large number of connection weights are iteratively tuned through stochastic-gradient …