Jonathan Ho
Jonathan Ho
Google Brain
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Cited by
Cited by
Generative adversarial imitation learning
J Ho, S Ermon
Advances in Neural Information Processing Systems, 4565-4573, 2016
Evolution strategies as a scalable alternative to reinforcement learning
T Salimans, J Ho, X Chen, S Sidor, I Sutskever
arXiv preprint arXiv:1703.03864, 2017
Motion planning with sequential convex optimization and convex collision checking
J Schulman, Y Duan, J Ho, A Lee, I Awwal, H Bradlow, J Pan, S Patil, ...
The International Journal of Robotics Research 33 (9), 1251-1270, 2014
One-shot imitation learning
Y Duan, M Andrychowicz, B Stadie, J Ho, J Schneider, I Sutskever, ...
Advances in Neural Information Processing Systems, 1087-1098, 2017
Finding locally optimal, collision-free trajectories with sequential convex optimization.
J Schulman, J Ho, AX Lee, I Awwal, H Bradlow, P Abbeel
Robotics: science and systems 9 (1), 1-10, 2013
Meta learning shared hierarchies
K Frans, J Ho, X Chen, P Abbeel, J Schulman
arXiv preprint arXiv:1710.09767, 2017
Denoising diffusion probabilistic models
J Ho, A Jain, P Abbeel
Advances in Neural Information Processing Systems 33, 6840-6851, 2020
Flow++: Improving flow-based generative models with variational dequantization and architecture design
J Ho, X Chen, A Srinivas, Y Duan, P Abbeel
International Conference on Machine Learning, 2019
Evolved policy gradients
R Houthooft, Y Chen, P Isola, B Stadie, F Wolski, J Ho, P Abbeel
Advances in Neural Information Processing Systems, 5405-5414, 2018
Tracking deformable objects with point clouds
J Schulman, A Lee, J Ho, P Abbeel
2013 IEEE International Conference on Robotics and Automation, 1130-1137, 2013
Axial attention in multidimensional transformers
J Ho, N Kalchbrenner, D Weissenborn, T Salimans
arXiv preprint arXiv:1912.12180, 2019
Learning from demonstrations through the use of non-rigid registration
J Schulman, J Ho, C Lee, P Abbeel
Robotics Research, 339-354, 2016
Model-Free Imitation Learning with Policy Optimization
J Ho, JK Gupta, S Ermon
International Conference on Machine Learning, 2016
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
FH Kingma, P Abbeel, J Ho
International Conference on Machine Learning, 2019
Variational diffusion models
DP Kingma, T Salimans, B Poole, J Ho
arXiv preprint arXiv:2107.00630, 2021
Image super-resolution via iterative refinement
C Saharia, J Ho, W Chan, T Salimans, DJ Fleet, M Norouzi
arXiv preprint arXiv:2104.07636, 2021
Compression with Flows via Local Bits-Back Coding
J Ho, E Lohn, P Abbeel
Advances in Neural Information Processing Systems, 3874-3883, 2019
Generative models
A Karpathy, P Abbeel, G Brockman, P Chen, V Cheung, R Duan, ...
OpenAI, 2016
Learning to efficiently sample from diffusion probabilistic models
D Watson, J Ho, M Norouzi, W Chan
arXiv preprint arXiv:2106.03802, 2021
Cascaded Diffusion Models for High Fidelity Image Generation
J Ho, C Saharia, W Chan, DJ Fleet, M Norouzi, T Salimans
arXiv preprint arXiv:2106.15282, 2021
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