David Dohan

According to our database, David Dohan authored at least 23 papers between 1998 and 2023.

Collaborative distances:

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

2023

PaLM: Scaling Language Modeling with Pathways.

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel

J. Mach. Learn. Res., 2023

Training Chain-of-Thought via Latent-Variable Inference.

Matthew Douglas Hoffman, Du Phan, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous

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

EvoPrompting: Language Models for Code-Level Neural Architecture Search.

Angelica Chen, David Dohan, David R. So

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

Large Language Models Can Be Easily Distracted by Irrelevant Context.

Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H. Chi, Nathanael Schärli, Denny Zhou

Proceedings of the International Conference on Machine Learning, 2023

2022

Language Model Cascades.

David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A. Saurous, Jascha Sohl-Dickstein, Kevin Murphy, Charles Sutton

CoRR, 2022

Solving Quantitative Reasoning Problems with Language Models.

Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Learning Universal Hyperparameter Optimizers with Transformers.

Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Richard Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Show Your Work: Scratchpads for Intermediate Computation with Language Models.

Maxwell I. Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena

CoRR, 2021

Program Synthesis with Large Language Models.

Jacob Austin, Augustus Odena, Maxwell I. Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, Charles Sutton

CoRR, 2021

Improving Protein Function Annotation via Unsupervised Pre-training: Robustness, Efficiency, and Insights.

David Dohan, Andreea Gane, Maxwell L. Bileschi, David Belanger, Lucy J. Colwell

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Latent Programmer: Discrete Latent Codes for Program Synthesis.

Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer

Proceedings of the 38th International Conference on Machine Learning, 2021

Rethinking Attention with Performers.

Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamás Sarlós, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J. Colwell, Adrian Weller

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Is Transfer Learning Necessary for Protein Landscape Prediction?

Amir Shanehsazzadeh, David Belanger, David Dohan

CoRR, 2020

Fixed-Length Protein Embeddings using Contextual Lenses.

Amir Shanehsazzadeh, David Belanger, David Dohan

CoRR, 2020

Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers.

Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Jared Davis, Tamás Sarlós, David Belanger, Lucy J. Colwell, Adrian Weller

CoRR, 2020

Amortized Bayesian Optimization over Discrete Spaces.

Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Population-Based Black-Box Optimization for Biological Sequence Design.

Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy J. Colwell, D. Sculley

Proceedings of the 37th International Conference on Machine Learning, 2020

Model-based reinforcement learning for biological sequence design.

Christof Angermüller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy J. Colwell

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension.

Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le

Proceedings of the 6th International Conference on Learning Representations, 2018

Evolving modular neural sequence architectures with genetic programming.

David Dohan, David R. So, Quoc V. Le

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

2017

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks.

Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2015

Learning Hierarchical Semantic Segmentations of LIDAR Data.

David Dohan, Brian Matejek, Thomas A. Funkhouser

Proceedings of the 2015 International Conference on 3D Vision, 2015

1998

The wonder of it all.

David Dohan

Proceedings of the ACM SIGGRAPH 98 Conference Abstracts and Applications, 1998