Ho, Jonathan

Denoising Diffusion Probabilistic Models

Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv

Epigenetics and DNA MethylationGene expression and cancer classification

Cited by 8,712

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 2,528

Generative adversarial imitation learning

Ho, J., & Ermon, S. (2016) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 1,942

Image Super-Resolution Via Iterative Refinement

Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., & Norouzi, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Image Processing TechniquesGenerative Adversarial Networks and Image Synthesis

Cited by 1,829

Image Super-Resolution via Iterative Refinement

Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., & Norouzi, M. (2021) · arXiv

Advanced Image Processing TechniquesGenerative Adversarial Networks and Image Synthesis

Cited by 1,829

Palette: Image-to-Image Diffusion Models

Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., & Norouzi, M. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings

Generative Adversarial Networks and Image SynthesisImage and Signal Denoising Methods

Cited by 1,717

Classifier-Free Diffusion Guidance

Ho, J., & Salimans, T. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 1,126

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Salimans, T., Ho, J., Chen, X., Sidor, S., & Sutskever, I. (2017) · arXiv

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 1,027

Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding

Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, K., Lopes, R. G., Ayan, B. K., Salimans, T., et al. (2022) · Advances in Neural Information Processing Systems 35

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 559

Cascaded Diffusion Models for High Fidelity Image Generation

Ho, J., Saharia, C., Chan, W., Fleet, D. J., Norouzi, M., & Salimans, T. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 514

Imagen Video: High Definition Video Generation with Diffusion Models

Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., et al. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 399

Variational Diffusion Models

Kingma, D. P., Salimans, T., Poole, B., & Ho, J. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisImage and Signal Denoising Methods

Cited by 374

Axial Attention in Multidimensional Transformers

Ho, J., Kalchbrenner, N., Weissenborn, D., & Salimans, T. (2019) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 370

Video Diffusion Models

Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., & Fleet, D. J. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 301

Progressive Distillation for Fast Sampling of Diffusion Models

Salimans, T., & Ho, J. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 287

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

Ho, J., Chen, X., Srinivas, A., Duan, Y., & Abbeel, P. (2019) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 245

Learning from Demonstrations Through the Use of Non-rigid Registration

Schulman, J., Ho, J., Lee, C., & Abbeel, P. (2016) · Springer Tracts in Advanced Robotics

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 133