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2020 – today
- 2023
[i16]Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H. Chi, Nathanael Schärli, Denny Zhou:
Large Language Models Can Be Easily Distracted by Irrelevant Context. CoRR abs/2302.00093 (2023)- [i15]Angelica Chen, David M. Dohan, David R. So:
EvoPrompting: Language Models for Code-Level Neural Architecture Search. CoRR abs/2302.14838 (2023) - 2022
- [c13]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:
Towards Learning Universal Hyperparameter Optimizers with Transformers. NeurIPS 2022 - [c12]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:
Solving Quantitative Reasoning Problems with Language Models. NeurIPS 2022 - [i14]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:
PaLM: Scaling Language Modeling with Pathways. CoRR abs/2204.02311 (2022) - [i13]Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas:
Towards Learning Universal Hyperparameter Optimizers with Transformers. CoRR abs/2205.13320 (2022) - [i12]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:
Solving Quantitative Reasoning Problems with Language Models. CoRR abs/2206.14858 (2022) - [i11]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:
Language Model Cascades. CoRR abs/2207.10342 (2022) - 2021
- [c11]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:
Rethinking Attention with Performers. ICLR 2021 - [c10]Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer:
Latent Programmer: Discrete Latent Codes for Program Synthesis. ICML 2021: 4308-4318 - [c9]David Dohan, Andreea Gane, Maxwell L. Bileschi, David Belanger, Lucy J. Colwell:
Improving Protein Function Annotation via Unsupervised Pre-training: Robustness, Efficiency, and Insights. KDD 2021: 2782-2791 - [i10]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:
Program Synthesis with Large Language Models. CoRR abs/2108.07732 (2021) - [i9]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:
Show Your Work: Scratchpads for Intermediate Computation with Language Models. CoRR abs/2112.00114 (2021) - 2020
- [c8]Christof Angermüller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy J. Colwell:
Model-based reinforcement learning for biological sequence design. ICLR 2020 - [c7]Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy J. Colwell, D. Sculley:
Population-Based Black-Box Optimization for Biological Sequence Design. ICML 2020: 324-334 - [c6]Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy:
Amortized Bayesian Optimization over Discrete Spaces. UAI 2020: 769-778 - [i8]Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy J. Colwell, D. Sculley:
Population-Based Black-Box Optimization for Biological Sequence Design. CoRR abs/2006.03227 (2020) - [i7]Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Jared Davis, Tamás Sarlós, David Belanger, Lucy J. Colwell, Adrian Weller:
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers. CoRR abs/2006.03555 (2020) - [i6]Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamás Sarlós, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy J. Colwell, Adrian Weller:
Rethinking Attention with Performers. CoRR abs/2009.14794 (2020) - [i5]Amir Shanehsazzadeh, David Belanger, David Dohan:
Fixed-Length Protein Embeddings using Contextual Lenses. CoRR abs/2010.15065 (2020) - [i4]Amir Shanehsazzadeh, David Belanger, David Dohan:
Is Transfer Learning Necessary for Protein Landscape Prediction? CoRR abs/2011.03443 (2020) - [i3]Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer:
Latent Programmer: Discrete Latent Codes for Program Synthesis. CoRR abs/2012.00377 (2020)
2010 – 2019
- 2018
- [c5]David Dohan, David R. So, Quoc V. Le:
Evolving modular neural sequence architectures with genetic programming. GECCO (Companion) 2018: 37-38 - [c4]Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le:
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension. ICLR (Poster) 2018 - [i2]Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le:
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension. CoRR abs/1804.09541 (2018) - 2017
- [c3]Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan:
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. CVPR 2017: 95-104 - 2016
- [i1]Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan:
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. CoRR abs/1612.05424 (2016) - 2015
- [c2]David Dohan, Brian Matejek, Thomas A. Funkhouser:
Learning Hierarchical Semantic Segmentations of LIDAR Data. 3DV 2015: 273-281
1990 – 1999
- 1998
- [c1]David Dohan:
The wonder of it all. SIGGRAPH Abstracts and Applications 1998: 332
Coauthor Index
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last updated on 2023-05-13 04:06 CEST by the dblp team
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