Ricky T. Q. Chen
40 papers · 2018–2025 · 5 conferences · across top CS/AI conferences
Achievements
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🌍 Conference Polyglot (5) 🏃 Academic Marathon (7) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (10)
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Cross-Pollinator
(10)
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Renaissance Researcher
(6)
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Taxonomy Completionist
(28)
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Dynamic Duo
(12)
👑
Triple Crown
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Topic Evolution
🗃️
Keyword Collector
(87)
💎
Century Club
(40)
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Prolific Year
(5)
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Unstoppable
(8)
Conferences
ICLR (15)
NIPS (12)
ICML (9)
AISTATS (2)
UAI (2)
Top co-authors
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 Schrödinger 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 Schrödinger 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, that’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