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Towards Understanding Grokking: An Effective Theory of Representation Learning



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

* 20 pages, 16 figures 

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Pareto-optimal clustering with the primal deterministic information bottleneck



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

* 26 pages, 11 figures 

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AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations



Ziming Liu , Varun Madhavan , Max Tegmark

* 17 pages, 10 figures 

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Biological error correction codes generate fault-tolerant neural networks



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


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



Ziming Liu , Yunyue Chen , Yuanqi Du , Max Tegmark

* 10 pages, 3 figures, 4 tables 

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Machine-learning hidden symmetries



Ziming Liu , Max Tegmark

* 9 pages, 3 figs 

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Machine-Learning media bias



Samantha D'Alonzo , Max Tegmark

* 29 pages, 23 figs; data available at https://space.mit.edu/home/tegmark/phrasebias.html 

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



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

* 17 pages, 7 figs, 2 tables; typo correction 

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AI Poincaré: Machine Learning Conservation Laws from Trajectories



Ziming Liu , Max Tegmark

* 5 pages, 3 figs 

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