Advances in deep learning have greatly improved structure prediction of molecules.
However, many macroscopic observations that are important for real-world applications are …

Recent advances in quantum computers are demonstrating the ability to solve problems at a
scale beyond brute force classical simulation. As such, a widespread interest in quantum …

A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

Conventional wisdom in deep learning states that increasing depth improves
expressiveness but complicates optimization. This paper suggests that, sometimes …

Gradient-based optimization algorithms can be studied from the perspective of limiting
ordinary differential equations (ODEs). Motivated by the fact that existing ODEs do not …

We study the underdamped Langevin diffusion when the log of the target distribution is
smooth and strongly concave. We present a MCMC algorithm based on its discretization and …

Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when
used to train deep neural networks, but the precise manner in which this occurs has thus far …

In this paper, we study the problem of sampling from a given probability density function that
is known to be smooth and strongly log-concave. We analyze several methods of …

This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

Can we accelerate the convergence of gradient descent without changing the algorithm—
just by judiciously choosing stepsizes? Surprisingly, we show that the answer is yes. Our …