Search Results for author: Will Grathwohl

Found 16 papers, 10 papers with code

Denoising Diffusion Samplers

no code implementations27 Feb 2023 Francisco Vargas, Will Grathwohl, Arnaud Doucet

Denoising Diffusion Samplers (DDS) are obtained by approximating the corresponding time-reversal.


Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

2 code implementations22 Feb 2023 Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Grathwohl

In this work, we build upon these ideas using the score-based interpretation of diffusion models, and explore alternative ways to condition, modify, and reuse diffusion models for tasks involving compositional generation and guidance.

Text-to-Image Generation

Continuous diffusion for categorical data

no code implementations28 Nov 2022 Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Leblond, Will Grathwohl, Jonas Adler

Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement.

Language Modelling

Score-Based Diffusion meets Annealed Importance Sampling

1 code implementation16 Aug 2022 Arnaud Doucet, Will Grathwohl, Alexander G. D. G. Matthews, Heiko Strathmann

To obtain an importance sampling estimate of the marginal likelihood, AIS introduces an extended target distribution to reweight the Markov chain proposal.

Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data

1 code implementation19 Jul 2021 Jacob Kelly, Richard Zemel, Will Grathwohl

We find our models are capable of both accurate, calibrated predictions and high-quality conditional synthesis of novel attribute combinations.


Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

1 code implementation8 Feb 2021 Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables.

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling

1 code implementation ICML 2020 Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Richard Zemel

We estimate the Stein discrepancy between the data density $p(x)$ and the model density $q(x)$ defined by a vector function of the data.

Invertible Residual Networks

5 code implementations2 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.

Density Estimation General Classification +1

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

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.

Density Estimation Image Generation +1

Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders

no code implementations14 Dec 2016 Will Grathwohl, Aaron Wilson

In this paper we propose a probabilistic approach for learning separable representations of object identity and pose information using unsupervised video data.

Object Transfer Learning +1

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