Max Tegmark

According to our database, Max Tegmark authored at least 54 papers between 1994 and 2024.

Collaborative distances:

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

2024

GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory.

David D. Baek, Ziming Liu, Max Tegmark

CoRR, 2024

A Resource Model For Neural Scaling Law.

Jinyeop Song, Ziming Liu, Max Tegmark, Jeff Gore

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

Black-Box Access is Insufficient for Rigorous AI Audits.

Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Alexander Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell

CoRR, 2024

2023

Precision Machine Learning.

Eric J. Michaud, Ziming Liu, Max Tegmark

Entropy, January, 2023

Generating Interpretable Networks using Hypernetworks.

Isaac Liao, Ziming Liu, Max Tegmark

CoRR, 2023

Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks.

Ziming Liu, Mikail Khona, Ila R. Fiete, Max Tegmark

CoRR, 2023

The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets.

Samuel Marks, Max Tegmark

CoRR, 2023

Divide-and-Conquer Dynamics in AI-Driven Disempowerment.

Peter S. Park, Max Tegmark

CoRR, 2023

Grokking as Compression: A Nonlinear Complexity Perspective.

Ziming Liu, Ziqian Zhong, Max Tegmark

CoRR, 2023

A Neural Scaling Law from Lottery Ticket Ensembling.

Ziming Liu, Max Tegmark

CoRR, 2023

Language Models Represent Space and Time.

Wes Gurnee, Max Tegmark

CoRR, 2023

Provably safe systems: the only path to controllable AGI.

Max Tegmark, Steve Omohundro

CoRR, 2023

Discovering New Interpretable Conservation Laws as Sparse Invariants.

Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam Silva, Max Tegmark

CoRR, 2023

Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability.

Ziming Liu, Eric Gan, Max Tegmark

CoRR, 2023

GenPhys: From Physical Processes to Generative Models.

Ziming Liu, Di Luo, Yilun Xu, Tommi S. Jaakkola, Max Tegmark

CoRR, 2023

The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks.

Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas

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

PFGM++: Unlocking the Potential of Physics-Inspired Generative Models.

Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola

Proceedings of the International Conference on Machine Learning, 2023

Omnigrok: Grokking Beyond Algorithmic Data.

Ziming Liu, Eric J. Michaud, Max Tegmark

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Pareto-Optimal Clustering with the Primal Deterministic Information Bottleneck.

Andrew K. Tan, Max Tegmark, Isaac L. Chuang

Entropy, 2022

AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations.

Ziming Liu, Varun Madhavan, Max Tegmark

CoRR, 2022

Biological error correction codes generate fault-tolerant neural networks.

Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Max Tegmark, Isaac L. Chuang

CoRR, 2022

Toward a more accurate 3D atlas of C. elegans neurons.

Michael Skuhersky, Tailin Wu, Eviatar Yemini, Amin Nejatbakhsh, Edward S. Boyden, Max Tegmark

BMC Bioinform., 2022

Poisson Flow Generative Models.

Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola

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

2021

Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning.

Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark

CoRR, 2021

Machine-learning hidden symmetries.

Ziming Liu, Max Tegmark

CoRR, 2021

Machine-Learning media bias.

Samantha D'Alonzo, Max Tegmark

CoRR, 2021

Machine-Learning Non-Conservative Dynamics for New-Physics Detection.

Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu

CoRR, 2021

2020

Pareto-Optimal Data Compression for Binary Classification Tasks.

Max Tegmark, Tailin Wu

Entropy, 2020

AI Poincaré: Machine Learning Conservation Laws from Trajectories.

Ziming Liu, Max Tegmark

CoRR, 2020

Symbolic Pregression: Discovering Physical Laws from Raw Distorted Video.

Silviu-Marian Udrescu, Max Tegmark

CoRR, 2020

AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity.

Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark

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

2019

Gated Orthogonal Recurrent Units: On Learning to Forget.

Li Jing, Çaglar Gülçehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio

Neural Comput., 2019

Learnability for the Information Bottleneck.

Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark

Entropy, 2019

AI Feynman: a Physics-Inspired Method for Symbolic Regression.

Silviu-Marian Udrescu, Max Tegmark

CoRR, 2019

The role of artificial intelligence in achieving the Sustainable Development Goals.

Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, Simone Langhans, Max Tegmark, Francesco Fuso Nerini

CoRR, 2019

2018

Toward an AI Physicist for Unsupervised Learning.

Tailin Wu, Max Tegmark

CoRR, 2018

Meta-learning autoencoders for few-shot prediction.

Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark

CoRR, 2018

The power of deeper networks for expressing natural functions.

David Rolnick, Max Tegmark

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

2017

Critical Behavior in Physics and Probabilistic Formal Languages.

Henry W. Lin, Max Tegmark

Entropy, 2017

On the Impossibility of Supersized Machines.

Ben Garfinkel, Miles Brundage, Daniel Filan, Carrick Flynn, Jelena Luketina, Michael Page, Anders Sandberg, Andrew Snyder-Beattie, Max Tegmark

CoRR, 2017

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs.

Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Improved Measures of Integrated Information.

Max Tegmark

PLoS Comput. Biol., 2016

Why does deep and cheap learning work so well?

Henry W. Lin, Max Tegmark

CoRR, 2016

Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language.

Henry Lin, Max Tegmark

CoRR, 2016

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN.

Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Max Tegmark, Marin Soljacic

CoRR, 2016

2015

Nuclear War from a Cosmic Perspective.

Max Tegmark

CoRR, 2015

Research Priorities for Robust and Beneficial Artificial Intelligence.

Stuart Russell, Daniel Dewey, Max Tegmark

AI Mag., 2015

Friendly Artificial Intelligence: The Physics Challenge.

Max Tegmark

Proceedings of the Artificial Intelligence and Ethics, 2015

2000

Why the brain is probably not a quantum computer.

Max Tegmark

Inf. Sci., 2000

1999

The importance of quantum decoherence in brain processes

Max Tegmark

CoRR, 1999

1994

An Elementary Proof That the Biharmonic Green Function of an Eccentric Ellipse Changes Sign.

Harold S. Shapiro, Max Tegmark

SIAM Rev., 1994