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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.

no code implementations • 6 Oct 2022 • Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le

We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale.

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.

no code implementations • 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

This provides a means for unifying training and inference algorithms, and provides a route to construct an agglomeration of 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.

1 code implementation • 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.

1 code implementation • 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.

2 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.

2 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.

11 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 PhysioNet Challenge 2012 (mse (10^-3) metric)

Multivariate Time Series Forecasting
Multivariate Time Series Imputation
**+1**

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

4 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 CIFAR-10 (NLL metric)

51 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 Forecasting on MuJoCo

Multivariate Time Series Forecasting Multivariate Time Series Imputation

9 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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