Jonathan Ho

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berkeley college ∙ Stanford University

Denoising Diffusion Probabilistic Models

We present high quality image synthesis results using diffusion probabil...
06/19/2020 ∙ by Jonathan Ho, et al. ∙ 6 ∙ share

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Axial Attention in Multidimensional Transformers

We propose Axial Transformers, a self-attention-based autoregressive mod...
12/20/2019 ∙ by Jonathan Ho, et al. ∙ 0 ∙ share

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Compression with Flows via Local Bits-Back Coding

Likelihood-based generative models are the backbones of lossless compres...
05/21/2019 ∙ by Jonathan Ho, et al. ∙ 1 ∙ share

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Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables

The bits-back argument suggests that latent variable models can be turne...
05/16/2019 ∙ by Friso H. Kingma, et al. ∙ 11 ∙ share

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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Flow-based generative models are powerful exact likelihood models with e...
02/01/2019 ∙ by Jonathan Ho, et al. ∙ 97 ∙ share

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Evolved Policy Gradients

We propose a meta-learning approach for learning gradient-based reinforc...
02/13/2018 ∙ by Rein Houthooft, et al. ∙ 0 ∙ share

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One-Shot Imitation Learning

Imitation learning has been commonly applied to solve different tasks in...
03/21/2017 ∙ by Yan Duan, et al. ∙ 0 ∙ share

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Evolution Strategies as a Scalable Alternative to Reinforcement Learning

We explore the use of Evolution Strategies (ES), a class of black box op...
03/10/2017 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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Generative Adversarial Imitation Learning

Consider learning a policy from example expert behavior, without interac...
06/10/2016 ∙ by Jonathan Ho, et al. ∙ 0 ∙ share

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Jonathan Ho

Featured Co-authors

Pieter Abbeel

Xi Chen

Stefano Ermon

Ilya Sutskever

Phillip Isola

Wojciech Zaremba

Tim Salimans

Nal Kalchbrenner

Yan Duan

Dirk Weissenborn

Marcin Andrychowicz

Denoising Diffusion Probabilistic Models

Axial Attention in Multidimensional Transformers

Compression with Flows via Local Bits-Back Coding

Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Evolved Policy Gradients

One-Shot Imitation Learning

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Generative Adversarial Imitation Learning