Ricky T. Q. Chen

Rodrigo A. Vargas-Hernández

Mach. Learn. Sci. Technol., 2024

Flow Matching Guide and Code.

[BibT_eX]

[DOI]

CoRR, 2024

Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective.

[BibT_eX]

[DOI]

CoRR, 2024

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions.

[BibT_eX]

[DOI]

CoRR, 2024

Generator Matching: Generative modeling with arbitrary Markov processes.

[BibT_eX]

[DOI]

CoRR, 2024

Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control.

[BibT_eX]

[DOI]

Michal Drozdzal

Brian Karrer

CoRR, 2024

Discrete Flow Matching.

[BibT_eX]

[DOI]

CoRR, 2024

Neural Optimal Transport with Lagrangian Costs.

[BibT_eX]

[DOI]

Aram-Alexandre Pooladian

CoRR, 2024

Reflected Schrödinger Bridge for Constrained Generative Modeling.

[BibT_eX]

[DOI]

CoRR, 2024

Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

FlowMM: Generating Materials with Riemannian Flow Matching.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Variational Schrödinger Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Bespoke Solvers for Generative Flow Models.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Generalized Schrödinger Bridge Matching.

[BibT_eX]

[DOI]

Evangelos A. Theodorou

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Flow Matching on General Geometries.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Stochastic Optimal Control Matching.

[BibT_eX]

[DOI]

CoRR, 2023

Guided Flows for Generative Modeling and Decision Making.

[BibT_eX]

[DOI]

CoRR, 2023

Training-free Linear Image Inversion via Flows.

[BibT_eX]

[DOI]

CoRR, 2023

Riemannian Flow Matching on General Geometries.

[BibT_eX]

[DOI]

CoRR, 2023

TaskMet: Task-driven Metric Learning for Model Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On Kinetic Optimal Probability Paths for Generative Models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Multisample Flow Matching: Straightening Flows with Minibatch Couplings.

[BibT_eX]

[DOI]

Aram-Alexandre Pooladian

Heli Ben-Hamu

Proceedings of the International Conference on Machine Learning, 2023

Latent State Marginalization as a Low-cost Approach for Improving Exploration.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Flow Matching for Generative Modeling.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Latent Discretization for Continuous-time Sequence Compression.

[BibT_eX]

[DOI]

CoRR, 2022

Unifying Generative Models with GFlowNets.

[BibT_eX]

[DOI]

CoRR, 2022

Neural Conservation Laws: A Divergence-Free Perspective.

[BibT_eX]

[DOI]

Jack Richter-Powell

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Theseus: A Library for Differentiable Nonlinear Optimization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Semi-Discrete Normalizing Flows through Differentiable Tessellation.

[BibT_eX]

[DOI]

Maximilian Nickel

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Matching Normalizing Flows and Probability Paths on Manifolds.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

"Hey, that's not an ODE": Faster ODE Adjoints via Seminorms.

[BibT_eX]

[DOI]

Patrick Kidger

Terry J. Lyons

Proceedings of the 38th International Conference on Machine Learning, 2021

Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Neural Spatio-Temporal Point Processes.

[BibT_eX]

[DOI]

Maximilian Nickel

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning Neural Event Functions for Ordinary Differential Equations.

[BibT_eX]

[DOI]

Maximilian Nickel

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code.

[BibT_eX]

[DOI]

Patrick Kidger

Terry J. Lyons

CoRR, 2020

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering.

[BibT_eX]

[DOI]

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

Scalable Gradients for Stochastic Differential Equations.

[BibT_eX]

[DOI]

Xuechen Li

Ting-Kam Leonard Wong

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Neural Networks with Cheap Differential Operators.

[BibT_eX]

[DOI]

CoRR, 2019

Latent ODEs for Irregularly-Sampled Time Series.

[BibT_eX]

[DOI]

Yulia Rubanova

CoRR, 2019

Residual Flows for Invertible Generative Modeling.

[BibT_eX]

[DOI]

CoRR, 2019

Invertible Residual Networks.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

Scalable Gradients and Variational Inference for Stochastic Differential Equations.

[BibT_eX]

[DOI]

Xuechen Li

Ting-Kam Leonard Wong