Ricky T. Q. Chen | DeepAI

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∙ 06/11/2023

On Kinetic Optimal Probability Paths for Generative Models

Recent successful generative models are trained by fitting a neural netw...

0 Neta Shaul, et al. ∙

∙ 02/11/2023

Distributional GFlowNets with Quantile Flows

Generative Flow Networks (GFlowNets) are a new family of probabilistic s...

0 Dinghuai Zhang, et al. ∙

∙ 02/07/2023

Riemannian Flow Matching on General Geometries

We propose Riemannian Flow Matching (RFM), a simple yet powerful framewo...

0 Ricky T. Q. Chen, et al. ∙

∙ 12/28/2022

Latent Discretization for Continuous-time Sequence Compression

Neural compression offers a domain-agnostic approach to creating codecs ...

0 Ricky T. Q. Chen, et al. ∙

∙ 10/06/2022

Flow Matching for Generative Modeling

We introduce a new paradigm for generative modeling built on Continuous ...

2 Yaron Lipman, et al. ∙

∙ 10/04/2022

Neural Conservation Laws: A Divergence-Free Perspective

We investigate the parameterization of deep neural networks that by desi...

0 Jack Richter-Powell, et al. ∙

∙ 10/03/2022

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

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework...

0 Dinghuai Zhang, et al. ∙

∙ 09/06/2022

Unifying Generative Models with GFlowNets

There are many frameworks for deep generative modeling, each often prese...

2 Dinghuai Zhang, et al. ∙

∙ 07/11/2022

Matching Normalizing Flows and Probability Paths on Manifolds

Continuous Normalizing Flows (CNFs) are a class of generative models tha...

12 Heli Ben-Hamu, et al. ∙

∙ 03/14/2022

Semi-Discrete Normalizing Flows through Differentiable Tessellation

Mapping between discrete and continuous distributions is a difficult tas...

0 Ricky T. Q. Chen, et al. ∙

∙ 02/12/2021

Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations

We perform scalable approximate inference in a recently-proposed family ...

24 Winnie Xu, et al. ∙

∙ 12/10/2020

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

Flow-based models are powerful tools for designing probabilistic models ...

0 Chin-Wei Huang, et al. ∙

∙ 11/09/2020

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering

Standard first-order stochastic optimization algorithms base their updat...

0 Ricky T. Q. Chen, et al. ∙

∙ 11/09/2020

Neural Spatio-Temporal Point Processes

We propose a new class of parameterizations for spatio-temporal point pr...

0 Ricky T. Q. Chen, et al. ∙

∙ 11/08/2020

Learning Neural Event Functions for Ordinary Differential Equations

The existing Neural ODE formulation relies on an explicit knowledge of t...

0 Ricky T. Q. Chen, et al. ∙

∙ 09/20/2020

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

Neural differential equations may be trained by backpropagating gradient...

0 Patrick Kidger, et al. ∙

∙ 04/01/2020

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

Standard variational lower bounds used to train latent variable models p...

8 Yucen Luo, et al. ∙

∙ 01/05/2020

Scalable Gradients for Stochastic Differential Equations

The adjoint sensitivity method scalably computes gradients of solutions ...

19 Xuechen Li, et al. ∙

∙ 12/08/2019

Neural Networks with Cheap Differential Operators

Gradients of neural networks can be computed efficiently for any archite...

36 Ricky T. Q. Chen, et al. ∙

∙ 07/08/2019

Latent ODEs for Irregularly-Sampled Time Series

Time series with non-uniform intervals occur in many applications, and a...

1 Yulia Rubanova, et al. ∙

∙ 06/06/2019

Residual Flows for Invertible Generative Modeling

Flow-based generative models parameterize probability distributions thro...

2 Ricky T. Q. Chen, et al. ∙

∙ 10/02/2018

FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

A promising class of generative models maps points from a simple distrib...

6 Will Grathwohl, et al. ∙

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