Derivatives play a critical role in computational statistics, examples being Bayesian
inference using Hamiltonian Monte Carlo sampling and the training of neural networks …

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine
learning. Automatic differentiation (AD), also called algorithmic differentiation or simply" auto …

JuMP is an open-source modeling language that allows users to express a wide range of
optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and …

We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD)
featuring performance competitive with low-level languages like C++. Unlike recently …

Algorithmic differentiation (AD), also known as automatic differentiation, is a technology for
accurate and efficient evaluation of derivatives of a function given as a computer model. The …

We explore the interplay between architectures and algorithm design in the context of
shared-memory platforms and a specific graph problem of central importance in scientific …

Computing derivatives of tensor expressions, also known as tensor calculus, is a
fundamental task in machine learning. A key concern is the efficiency of evaluating the …

Optimization is an integral part of most machine learning systems and most numerical
optimization schemes rely on the computation of derivatives. Therefore, frameworks for …

We present a suite of fast and effective algorithms, encapsulated in a software package
called ColPack, for a variety of graph coloring and related problems. Many of the coloring …

We show that recent innovations in deep reinforcement learning can effectively color very
large graphs--a well-known NP-hard problem with clear commercial applications. Because …