no code implementations • 2 Mar 2024 • Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali Thabet, Albert Pumarola, Yaron Lipman

This paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver distillation approach to improve sample efficiency of Diffusion and Flow models.

no code implementations • 6 Jan 2024 • Wei Deng, Yu Chen, Nicole Tianjiao Yang, Hengrong Du, Qi Feng, Ricky T. Q. Chen

Diffusion models have become the go-to method for large-scale generative models in real-world applications.

no code implementations • NeurIPS 2023 • Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, Brandon Amos

We propose take the task loss signal one level deeper than the parameters of the model and use it to learn the parameters of the loss function the model is trained on, which can be done by learning a metric in the prediction space.

1 code implementation • 4 Dec 2023 • Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen

Our work introduces Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control that stems from the same philosophy as the conditional score matching loss for diffusion models.

no code implementations • 22 Nov 2023 • Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, Ricky T. Q. Chen

Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks.

no code implementations • 29 Oct 2023 • Neta Shaul, Juan Perez, Ricky T. Q. Chen, Ali Thabet, Albert Pumarola, Yaron Lipman

For example, a Bespoke solver for a CIFAR10 model produces samples with Fr\'echet Inception Distance (FID) of 2. 73 with 10 NFE, and gets to 1% of the Ground Truth (GT) FID (2. 59) for this model with only 20 NFE.

2 code implementations • 4 Oct 2023 • Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio

We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics.

1 code implementation • 3 Oct 2023 • Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen

Modern distribution matching algorithms for training diffusion or flow models directly prescribe the time evolution of the marginal distributions between two boundary distributions.

no code implementations • 25 Sep 2023 • Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer

Solving inverse problems without any training involves using a pretrained generative model and making appropriate modifications to the generation process to avoid finetuning of the generative model.

no code implementations • 11 Jun 2023 • Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matt Le, Yaron Lipman

We investigate Kinetic Optimal (KO) Gaussian paths and offer the following observations: (i) We show the KE takes a simplified form on the space of Gaussian paths, where the data is incorporated only through a single, one dimensional scalar function, called the \emph{data separation function}.

no code implementations • 28 Apr 2023 • Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen

Simulation-free methods for training continuous-time generative models construct probability paths that go between noise distributions and individual data samples.

1 code implementation • 11 Feb 2023 • Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron Courville, Yoshua Bengio

Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating complex combinatorial structure through a series of decision-making steps.

2 code implementations • 7 Feb 2023 • Ricky T. Q. Chen, Yaron Lipman

To extend to general geometries, we rely on the use of spectral decompositions to efficiently compute premetrics on the fly.

no code implementations • 28 Dec 2022 • Ricky T. Q. Chen, Matthew Le, Matthew Muckley, Maximilian Nickel, Karen Ullrich

We empirically verify our approach on multiple domains involving compression of video and motion capture sequences, showing that our approaches can automatically achieve reductions in bit rates by learning how to discretize.

1 code implementation • 6 Oct 2022 • Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le

These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization.

Ranked #5 on Density Estimation on CIFAR-10

1 code implementation • 4 Oct 2022 • 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.

1 code implementation • 3 Oct 2022 • Dinghuai Zhang, Aaron Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity.

no code implementations • 6 Sep 2022 • Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio

Our framework provides a means for unifying training and inference algorithms, and provides a route to shine a unifying light over many generative models.

1 code implementation • 19 Jul 2022 • Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam

We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision.

no code implementations • 11 Jul 2022 • Heli Ben-Hamu, samuel cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky T. Q. Chen, Yaron Lipman

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).

1 code implementation • 14 Mar 2022 • 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.

2 code implementations • 12 Feb 2021 • Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud

We perform scalable approximate inference in continuous-depth Bayesian neural networks.

no code implementations • 1 Jan 2021 • Patrick Kidger, Ricky T. Q. Chen, Terry Lyons

Neural differential equations may be trained by backpropagating gradients via the adjoint method, which is another differential equation typically solved using an adaptive-step-size numerical differential equation solver.

2 code implementations • ICLR 2021 • Chin-wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron Courville

Flow-based models are powerful tools for designing probabilistic models with tractable density.

no code implementations • NeurIPS Workshop ICBINB 2020 • Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig

Standard first-order stochastic optimization algorithms base their updates solely on the average mini-batch gradient, and it has been shown that tracking additional quantities such as the curvature can help de-sensitize common hyperparameters.

1 code implementation • ICLR 2021 • Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space.

1 code implementation • ICLR 2021 • Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

The existing Neural ODE formulation relies on an explicit knowledge of the termination time.

3 code implementations • 20 Sep 2020 • Patrick Kidger, Ricky T. Q. Chen, Terry Lyons

Neural differential equations may be trained by backpropagating gradients via the adjoint method, which is another differential equation typically solved using an adaptive-step-size numerical differential equation solver.

no code implementations • ICLR 2020 • Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen

Standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest.

4 code implementations • 5 Jan 2020 • Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud

The adjoint sensitivity method scalably computes gradients of solutions to ordinary differential equations.

Ranked #1 on Video Prediction on CMU Mocap-2

no code implementations • 8 Dec 2019 • Ricky T. Q. Chen, David Duvenaud

Gradients of neural networks can be computed efficiently for any architecture, but some applications require differential operators with higher time complexity.

no code implementations • pproximateinference AABI Symposium 2019 • Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David K. Duvenaud

We derive reverse-mode (or adjoint) automatic differentiation for solutions of stochastic differential equations (SDEs), allowing time-efficient and constant-memory computation of pathwise gradients, a continuous-time analogue of the reparameterization trick.

12 code implementations • 8 Jul 2019 • Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud

Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).

Ranked #1 on Multivariate Time Series Imputation on MuJoCo

Multivariate Time Series Forecasting
Multivariate Time Series Imputation
**+3**

4 code implementations • NeurIPS 2019 • Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen

Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood.

Ranked #2 on Image Generation on MNIST

5 code implementations • 2 Nov 2018 • Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen

We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation.

Ranked #5 on Image Generation on MNIST

7 code implementations • ICLR 2019 • Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

The result is a continuous-time invertible generative model with unbiased density estimation and one-pass sampling, while allowing unrestricted neural network architectures.

Ranked #1 on Density Estimation on UCI MINIBOONE

55 code implementations • NeurIPS 2018 • Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud

Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network.

Ranked #2 on Multivariate Time Series Imputation on MuJoCo

Multivariate Time Series Forecasting Multivariate Time Series Imputation

10 code implementations • NeurIPS 2018 • Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud

We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables.

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