We present a framework for video modeling based on denoising diffusion probabilistic
models that produces long-duration video completions in a variety of realistic environments …

Dataset distillation aims to learn a small synthetic dataset that preserves most of the
information from the original dataset. Dataset distillation can be formulated as a bi-level …

We present Brax, an open source library for rigid body simulation with a focus on
performance and parallelism on accelerators, written in JAX. We present results on a suite of …

Deep learning has revolutionized pattern recognition, introducing tools that power a wide
range of technologies in such diverse fields as computer vision, natural language …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

Graph neural networks (GNNs) are a popular class of machine learning models that have
been successfully applied to a range of problems. Their major advantage lies in their ability …

Flow-based generative models parameterize probability distributions through an invertible
transformation and can be trained by maximum likelihood. Invertible residual networks …

Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs),
serve as a fundamental building block for many sequence learning tasks, including machine …

LSTMs and other RNN variants have shown strong performance on character-level
language modeling. These models are typically trained using truncated backpropagation …

Text-to-image diffusion models have recently emerged at the forefront of image generation,
powered by very large-scale unsupervised or weakly supervised text-to-image training …