Follow
David Dohan
David Dohan
Google Brain
Verified email at google.com
Title
Cited by
Cited by
Year
Palm: Scaling language modeling with pathways
A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ...
Journal of Machine Learning Research 24 (240), 1-113, 2023
33202023
Unsupervised pixel-level domain adaptation with generative adversarial networks
K Bousmalis, N Silberman, D Dohan, D Erhan, D Krishnan
Proceedings of the IEEE conference on computer vision and pattern …, 2017
18182017
Qanet: Combining local convolution with global self-attention for reading comprehension
AW Yu, D Dohan, MT Luong, R Zhao, K Chen, M Norouzi, QV Le
International Conference on Learning Representations, 2018
1268*2018
Rethinking attention with performers
K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ...
International Conference on Learning Representations, 2021
12622021
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
7212022
Gpt-4 technical report
J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ...
arXiv preprint arXiv:2303.08774, 2023
6812023
Program synthesis with large language models
J Austin, A Odena, M Nye, M Bosma, H Michalewski, D Dohan, E Jiang, ...
arXiv preprint arXiv:2108.07732, 2021
6322021
Show your work: Scratchpads for intermediate computation with language models
M Nye, AJ Andreassen, G Gur-Ari, H Michalewski, J Austin, D Bieber, ...
arXiv preprint arXiv:2112.00114, 2021
3942021
Solving quantitative reasoning problems with language models
A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ...
Advances in Neural Information Processing Systems 35, 3843-3857, 2022
3912022
Large language models can be easily distracted by irrelevant context
F Shi, X Chen, K Misra, N Scales, D Dohan, EH Chi, N Schärli, D Zhou
International Conference on Machine Learning, 31210-31227, 2023
1232023
Model-based reinforcement learning for biological sequence design
C Angermueller, D Dohan, D Belanger, R Deshpande, K Murphy, ...
International conference on learning representations, 2019
1172019
Masked language modeling for proteins via linearly scalable long-context transformers
K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ...
arXiv preprint arXiv:2006.03555, 2020
832020
Palm: Scaling language modeling with pathways. arXiv 2022
A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ...
arXiv preprint arXiv:2204.02311 10, 2022
782022
Language model cascades
D Dohan, W Xu, A Lewkowycz, J Austin, D Bieber, RG Lopes, Y Wu, ...
arXiv preprint arXiv:2207.10342, 2022
622022
Chi, Nathanael Schärli, and Denny Zhou. 2023. Large language models can be easily distracted by irrelevant context
F Shi, X Chen, K Misra, N Scales, D Dohan
arXiv preprint arXiv:2302.00093, 2023
562023
Population-based black-box optimization for biological sequence design
C Angermueller, D Belanger, A Gane, Z Mariet, D Dohan, K Murphy, ...
International conference on machine learning, 324-334, 2020
462020
Is transfer learning necessary for protein landscape prediction?
A Shanehsazzadeh, D Belanger, D Dohan
NeurIPS workshop on Machine Learning in Structural Biology, 2020
462020
Learning hierarchical semantic segmentations of LIDAR data
D Dohan, B Matejek, T Funkhouser
2015 International Conference on 3D Vision, 273-281, 2015
442015
Amortized bayesian optimization over discrete spaces
K Swersky, Y Rubanova, D Dohan, K Murphy
Conference on Uncertainty in Artificial Intelligence, 769-778, 2020
372020
Evoprompting: Language models for code-level neural architecture search
A Chen, D Dohan, D So
Advances in Neural Information Processing Systems 36, 2024
352024
The system can't perform the operation now. Try again later.
Articles 1–20