← Authors

Ricky T. Q. Chen

40 papers · 2018–2025 · 5 conferences · across top CS/AI conferences

Achievements

๐Ÿฃ Hot Topic Early Bird ๐ŸŒ‰ Interdisciplinary Bridge ๐Ÿงญ Keyword Pioneer ๐ŸŒ Conference Polyglot (5) ๐Ÿƒ Academic Marathon (7) ๐Ÿ Cross-Pollinator (10) ๐ŸŒˆ Renaissance Researcher (6) ๐Ÿ—บ๏ธ Taxonomy Completionist (28) ๐Ÿค Dynamic Duo (12) ๐Ÿ‘‘ Triple Crown ๐Ÿงฌ Topic Evolution ๐Ÿ—ƒ๏ธ Keyword Collector (87) ๐Ÿ’Ž Century Club (40) โšก Prolific Year (5) ๐Ÿ”ฅ Unstoppable (8)

Conferences

ICLR (15) NIPS (12) ICML (9) AISTATS (2) UAI (2)

Top co-authors

Yaron Lipman (12) Brandon Amos (10) Maximilian Nickel (6) Neta Shaul (6) David K. Duvenaud (5) David Duvenaud (5) Carles Domingo-Enrich (5) Brian Karrer (5) Anuroop Sriram (4) Matthew Le (4)

Keywords

neural network (4) generative model (4) flow matching (3) ordinary differential equation (3) probability path (3) normalizing flow (3) stochastic differential equation (3) stochastic variational inference (2) continuous normalizing flow (2) invertible residual network (2) variational autoencoder (2) maximum likelihood (2) density estimation (2) discrete distribution (1) implicit differentiation (1) gradient computation (1) code generation (1) metric learning (1) stochastic process (1) mixture model (1)

Papers

Simulation-Free Differential Dynamics Through Neural Conservation Laws UAI 2025
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control ICLR 2025
Generator Matching: Generative modeling with arbitrary Markov processes ICLR 2025
FlowDec: A flow-based full-band general audio codec with high perceptual quality ICLR 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching ICML 2025
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective ICLR 2025
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions NIPS 2024
Stochastic Optimal Control Matching NIPS 2024
Discrete Flow Matching NIPS 2024
Bespoke Solvers for Generative Flow Models ICLR 2024
Flow Matching on General Geometries ICLR 2024
Generalized Schrรถdinger Bridge Matching ICLR 2024
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization ICLR 2024
Neural Optimal Transport with Lagrangian Costs UAI 2024
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models ICML 2024
FlowMM: Generating Materials with Riemannian Flow Matching ICML 2024
Variational Schrรถdinger Diffusion Models ICML 2024
Flow Matching for Generative Modeling ICLR 2023
TaskMet: Task-driven Metric Learning for Model Learning NIPS 2023
Latent State Marginalization as a Low-cost Approach for Improving Exploration ICLR 2023
Multisample Flow Matching: Straightening Flows with Minibatch Couplings ICML 2023
On Kinetic Optimal Probability Paths for Generative Models ICML 2023
Matching Normalizing Flows and Probability Paths on Manifolds ICML 2022
Neural Conservation Laws: A Divergence-Free Perspective NIPS 2022
Theseus: A Library for Differentiable Nonlinear Optimization NIPS 2022
Semi-Discrete Normalizing Flows through Differentiable Tessellation NIPS 2022
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations AISTATS 2022
Neural Spatio-Temporal Point Processes ICLR 2021
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization ICLR 2021
"Hey, thatโ€™s not an ODE": Faster ODE Adjoints via Seminorms ICML 2021
Learning Neural Event Functions for Ordinary Differential Equations ICLR 2021
Scalable Gradients for Stochastic Differential Equations AISTATS 2020
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models ICLR 2020
Neural Networks with Cheap Differential Operators NIPS 2019
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models ICLR 2019
Invertible Residual Networks ICML 2019
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series NIPS 2019
Residual Flows for Invertible Generative Modeling NIPS 2019
Isolating Sources of Disentanglement in Variational Autoencoders NIPS 2018
Neural Ordinary Differential Equations NIPS 2018