David Dohan

According to our database¹, David Dohan authored at least 24 papers between 1998 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

In proceedings

Links

Bibliography

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Trans. Mach. Learn. Res., 2023

PaLM: Scaling Language Modeling with Pathways.

J. Mach. Learn. Res., 2023

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

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.

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.

Proceedings of the International Conference on Machine Learning, 2023

Language Model Cascades.

Solving Quantitative Reasoning Problems with Language Models.

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.

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

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

Program Synthesis with Large Language Models.

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

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.

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

Rethinking Attention with Performers.

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

Is Transfer Learning Necessary for Protein Landscape Prediction?

Fixed-Length Protein Embeddings using Contextual Lenses.

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

Amortized Bayesian Optimization over Discrete Spaces.

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

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

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

Model-based reinforcement learning for biological sequence design.

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

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

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

Evolving modular neural sequence architectures with genetic programming.

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks.

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

Learning Hierarchical Semantic Segmentations of LIDAR Data.

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

The wonder of it all.

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

David Dohan

Timeline

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Bibliography

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