Jonathan Heek

According to our database, Jonathan Heek authored at least 14 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Model Integrity when Unlearning with T2I Diffusion Models.

Andrea Schioppa, Emiel Hoogeboom, Jonathan Heek

CoRR, 2024

Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion.

Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, Tim Salimans

CoRR, 2024

Multistep Distillation of Diffusion Models via Moment Matching.

Tim Salimans, Thomas Mensink, Jonathan Heek, Emiel Hoogeboom

CoRR, 2024

Multistep Consistency Models.

Jonathan Heek, Emiel Hoogeboom, Tim Salimans

CoRR, 2024

Rolling Diffusion Models.

David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets.

Jonas Ngnawé, Marianne Abemgnigni Njifon, Jonathan Heek, Yann N. Dauphin

CoRR, 2023

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution.

Mostafa Dehghani, Basil Mustafa, Josip Djolonga, Jonathan Heek, Matthias Minderer, Mathilde Caron, Andreas Steiner, Joan Puigcerver, Robert Geirhos, Ibrahim M. Alabdulmohsin, Avital Oliver, Piotr Padlewski, Alexey A. Gritsenko, Mario Lucic, Neil Houlsby

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficiently Scaling Transformer Inference.

Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Jonathan Heek, Kefan Xiao, Shivani Agrawal, Jeff Dean

Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

simple diffusion: End-to-end diffusion for high resolution images.

Emiel Hoogeboom, Jonathan Heek, Tim Salimans

Proceedings of the International Conference on Machine Learning, 2023

Scaling Vision Transformers to 22 Billion Parameters.

Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby

Proceedings of the International Conference on Machine Learning, 2023

2022

Efficiently Scaling Transformer Inference.

Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Anselm Levskaya, Jonathan Heek, Kefan Xiao, Shivani Agrawal, Jeff Dean

CoRR, 2022

2021

Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks.

Lasse Espeholt, Shreya Agrawal, Casper Kaae Sønderby, Manoj Kumar, Jonathan Heek, Carla Bromberg, Cenk Gazen, Jason Hickey, Aaron Bell, Nal Kalchbrenner

CoRR, 2021

2020

MetNet: A Neural Weather Model for Precipitation Forecasting.

Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver, Tim Salimans, Shreya Agrawal, Jason Hickey, Nal Kalchbrenner

CoRR, 2020

2019

Bayesian Inference for Large Scale Image Classification.

Jonathan Heek, Nal Kalchbrenner

CoRR, 2019