Eric J. Michaud

According to our database, Eric J. Michaud authored at least 10 papers between 2020 and 2024.

Collaborative distances:

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

2024

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.

Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller

CoRR, 2024

Opening the AI black box: program synthesis via mechanistic interpretability.

Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelic, Max Tegmark

CoRR, 2024

2023

Precision Machine Learning.

Eric J. Michaud, Ziming Liu, Max Tegmark

Entropy, January, 2023

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell

CoRR, 2023

The Quantization Model of Neural Scaling.

Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Omnigrok: Grokking Beyond Algorithmic Data.

Ziming Liu, Eric J. Michaud, Max Tegmark

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Towards Understanding Grokking: An Effective Theory of Representation Learning.

Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams

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

2020

Examining the Causal Structures of Deep Neural Networks Using Information Theory.

Scythia Marrow, Eric J. Michaud, Erik Hoel

Entropy, 2020

Understanding Learned Reward Functions.

Eric J. Michaud, Adam Gleave, Stuart Russell

CoRR, 2020

Examining the causal structures of deep neural networks using information theory.

Simon Mattsson, Eric J. Michaud, Erik Hoel

CoRR, 2020