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ORCIDEric J. Michaud
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2020 – today
- 2024
[i13]Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelic, Max Tegmark:
Opening the AI black box: program synthesis via mechanistic interpretability. CoRR abs/2402.05110 (2024)- [i12]Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller:
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models. CoRR abs/2403.19647 (2024) - [i11]Joshua Engels, Isaac Liao, Eric J. Michaud, Wes Gurnee, Max Tegmark:
Not All Language Model Features Are Linear. CoRR abs/2405.14860 (2024) - [i10]Xiaoman Delores Ding, Zifan Carl Guo, Eric J. Michaud, Ziming Liu, Max Tegmark:
Survival of the Fittest Representation: A Case Study with Modular Addition. CoRR abs/2405.17420 (2024) - [i9]Anish Mudide, Joshua Engels, Eric J. Michaud, Max Tegmark, Christian Schröder de Witt:
Efficient Dictionary Learning with Switch Sparse Autoencoders. CoRR abs/2410.08201 (2024) - [i8]Yuxiao Li, Eric J. Michaud, David D. Baek, Joshua Engels, Xiaoqing Sun, Max Tegmark:
The Geometry of Concepts: Sparse Autoencoder Feature Structure. CoRR abs/2410.19750 (2024) - 2023
- [j3]Eric J. Michaud, Ziming Liu, Max Tegmark
:
Precision Machine Learning. Entropy 25(1): 175 (2023) - [j2]Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Tong 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:
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. Trans. Mach. Learn. Res. 2023 (2023) - [c3]Ziming Liu, Eric J. Michaud, Max Tegmark:
Omnigrok: Grokking Beyond Algorithmic Data. ICLR 2023 - [c2]Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark:
The Quantization Model of Neural Scaling. NeurIPS 2023 - [i7]Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark:
The Quantization Model of Neural Scaling. CoRR abs/2303.13506 (2023) - [i6]Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Tong 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:
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. CoRR abs/2307.15217 (2023) - 2022
- [c1]Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams:
Towards Understanding Grokking: An Effective Theory of Representation Learning. NeurIPS 2022 - [i5]Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams:
Towards Understanding Grokking: An Effective Theory of Representation Learning. CoRR abs/2205.10343 (2022) - [i4]Ziming Liu, Eric J. Michaud, Max Tegmark:
Omnigrok: Grokking Beyond Algorithmic Data. CoRR abs/2210.01117 (2022) - [i3]Eric J. Michaud, Ziming Liu, Max Tegmark:
Precision Machine Learning. CoRR abs/2210.13447 (2022) - 2020
- [j1]Scythia Marrow, Eric J. Michaud, Erik Hoel:
Examining the Causal Structures of Deep Neural Networks Using Information Theory. Entropy 22(12): 1429 (2020) - [i2]Simon Mattsson, Eric J. Michaud, Erik Hoel:
Examining the causal structures of deep neural networks using information theory. CoRR abs/2010.13871 (2020) - [i1]Eric J. Michaud, Adam Gleave, Stuart Russell:
Understanding Learned Reward Functions. CoRR abs/2012.05862 (2020)
Coauthor Index
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