The wonder of it all. - David Dohan (SIGGRAPH Abstracts and Applications, 1998)

Learning Hierarchical Semantic Segmentations of LIDAR Data. - David Dohan; Brian Matejek; Thomas A. Funkhouser (3DV, 2015)

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. - Konstantinos Bousmalis; Nathan Silberman; David Dohan; Dumitru Erhan; Dilip Krishnan (CVPR, 2017)

Evolving modular neural sequence architectures with genetic programming. - David Dohan; David R. So; Quoc V. Le (GECCO (Companion), 2018)

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension. - Adams Wei Yu; David Dohan; Minh-Thang Luong; Rui Zhao; Kai Chen 010; Mohammad Norouzi 002; Quoc V. Le (ICLR (Poster), 2018)

Model-based reinforcement learning for biological sequence design. - Christof Angermüller; David Dohan; David Belanger 002; Ramya Deshpande; Kevin Murphy 002; Lucy J. Colwell (ICLR, 2020)

Population-Based Black-Box Optimization for Biological Sequence Design. - Christof Angermüller; David Belanger 002; Andreea Gane; Zelda Mariet; David Dohan; Kevin Murphy 002; Lucy J. Colwell; D. Sculley (ICML, 2020)

Amortized Bayesian Optimization over Discrete Spaces. - Kevin Swersky; Yulia Rubanova; David Dohan; Kevin Murphy 002 (UAI, 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 002; Lucy J. Colwell; Adrian Weller (CoRR, 2020)

Fixed-Length Protein Embeddings using Contextual Lenses. - Amir Shanehsazzadeh; David Belanger 002; David Dohan (CoRR, 2020)

Is Transfer Learning Necessary for Protein Landscape Prediction? - Amir Shanehsazzadeh; David Belanger 002; David Dohan (CoRR, 2020)

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; et al. (ICLR, 2021)

Latent Programmer: Discrete Latent Codes for Program Synthesis. - Joey Hong; David Dohan; Rishabh Singh; Charles Sutton; Manzil Zaheer (ICML, 2021)

Improving Protein Function Annotation via Unsupervised Pre-training: Robustness, Efficiency, and Insights. - David Dohan; Andreea Gane; Maxwell L. Bileschi; David Belanger 002; Lucy J. Colwell (KDD, 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)

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)

Towards Learning Universal Hyperparameter Optimizers with Transformers. - Yutian Chen 001; Xingyou Song; Chansoo Lee; Zi Wang; Richard Zhang 003; David Dohan; Kazuya Kawakami; Greg Kochanski; Arnaud Doucet; Marc'Aurelio Ranzato; Sagi Perel; Nando de Freitas (NeurIPS, 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; et al. (NeurIPS, 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 002; Charles Sutton (CoRR, 2022)

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 (ICML, 2023)

EvoPrompting: Language Models for Code-Level Neural Architecture Search. - Angelica Chen; David Dohan; David R. So (NeurIPS, 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 (NeurIPS, 2023)

PaLM: Scaling Language Modeling with Pathways. - Aakanksha Chowdhery; Sharan Narang; Jacob Devlin; Maarten Bosma; Gaurav Mishra; Adam Roberts; Paul Barham 001; Hyung Won Chung; Charles Sutton; Sebastian Gehrmann; Parker Schuh; Kensen Shi; et al. (J. Mach. Learn. Res., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

OpenAI o1 System Card. - Aaron Jaech; Adam Kalai; Adam Lerer; Adam Richardson; Ahmed El-Kishky; Aiden Low; Alec Helyar; Aleksander Madry; Alex Beutel; Alex Carney; Alex Iftimie; Alex Karpenko; et al. (CoRR, 2024)

Competitive Programming with Large Reasoning Models. - Ahmed El-Kishky; Alexander Wei; Andre Saraiva 001; Borys Minaiev; Daniel Selsam; David Dohan; Francis Song; Hunter Lightman; Ignasi Clavera Gilaberte; Jakub Pachocki; Jerry Tworek; Lorenz Kuhn; et al. (CoRR, 2025)