Jaehoon Lee

According to our database, Jaehoon Lee authored at least 18 papers between 2018 and 2023.

Collaborative distances:

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

2023

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

CoRR, 2023

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?

C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein

CoRR, 2023

Small-scale proxies for large-scale Transformer training instabilities.

Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith

CoRR, 2023

Kernel Regression with Infinite-Width Neural Networks on Millions of Examples.

Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek

CoRR, 2023

2022

Fast Neural Kernel Embeddings for General Activations.

Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi

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

2021

Explaining Neural Scaling Laws.

Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, Utkarsh Sharma

CoRR, 2021

Dataset Distillation with Infinitely Wide Convolutional Networks.

Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit.

Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek

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

2020

Towards NNGP-guided Neural Architecture Search.

Daniel S. Park, Jaehoon Lee, Daiyi Peng, Yuan Cao, Jascha Sohl-Dickstein

CoRR, 2020

On the infinite width limit of neural networks with a standard parameterization.

Jascha Sohl-Dickstein, Roman Novak, Samuel S. Schoenholz, Jaehoon Lee

CoRR, 2020

Finite Versus Infinite Neural Networks: an Empirical Study.

Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Neural Tangents: Fast and Easy Infinite Neural Networks in Python.

Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz

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

2019

Measuring the Effects of Data Parallelism on Neural Network Training.

Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl

J. Mach. Learn. Res., 2019

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.

Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Jascha Sohl-Dickstein, Jeffrey Pennington

CoRR, 2019

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.

Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes.

Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes.

Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

CoRR, 2018

Deep Neural Networks as Gaussian Processes.

Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein

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