175 citations · 995 across the 28 of their papers we have counts for
8 papers · 1 filter
Latent Discretization for Continuous-time Sequence Compression
Ricky T. Q. Chen, Matthew Le, Matthew Muckley +2
Neural compression offers a domain-agnostic approach to creating codecs for lossy or lossless compression via deep generative models. For sequence compression, however, most deep s…
Flow Matching for Generative Modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu +2
We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the not…
Neural Conservation Laws: A Divergence-Free Perspective
Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen
We investigate the parameterization of deep neural networks that by design satisfy the continuity equation, a fundamental conservation law. This is enabled by the observation that…
Latent State Marginalization as a Low-cost Approach for Improving Exploration
Dinghuai Zhang, Aaron Courville, Yoshua Bengio +3
While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilisti…
Unifying Generative Models with GFlowNets and Beyond
Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin +1
There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference methods. Here, we demonstrate the connections…
Matching Normalizing Flows and Probability Paths on Manifolds
Heli Ben-Hamu, Samuel Cohen, Joey Bose +5
Continuous Normalizing Flows (CNFs) are a class of generative models that transform a prior distribution to a model distribution by solving an ordinary differential equation (ODE).…
Theseus: A Library for Differentiable Nonlinear Optimization
Luis Pineda, Taosha Fan, Maurizio Monge +10
We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framew…
Semi-Discrete Normalizing Flows through Differentiable Tessellation
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Mapping between discrete and continuous distributions is a difficult task and many have had to resort to heuristical approaches. We propose a tessellation-based approach that direc…