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AI Poincaré: Machine Learning Conservation Laws from Trajectories

Nov 09, 2020
Ziming Liu, Max Tegmark

* 5 pages, 3 figs 

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

Jun 18, 2020
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark

* 16 pages, 6 figs 

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Symbolic Pregression: Discovering Physical Laws from Raw Distorted Video

May 19, 2020
Silviu-Marian Udrescu, Max Tegmark

* 12 pages, including 6 figs 

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Pareto-optimal data compression for binary classification tasks

Aug 23, 2019
Max Tegmark, Tailin Wu

* 15 pages, 8 figs 

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Learnability for the Information Bottleneck

Jul 17, 2019
Tailin Wu, Ian Fischer, Isaac L. Chuang, Max Tegmark

* Accepted at UAI 2019 

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AI Feynman: a Physics-Inspired Method for Symbolic Regression

May 27, 2019
Silviu-Marian Udrescu, Max Tegmark

* 14 pages, 2 figs. Our Feynman Symbolic Regression Database for benchmarking can be downloaded at 

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The role of artificial intelligence in achieving the Sustainable Development Goals

Apr 30, 2019
Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, Simone Langhans, Max Tegmark, Francesco Fuso Nerini

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Latent Representations of Dynamical Systems: When Two is Better Than One

Feb 20, 2019
Max Tegmark

* Improved references and explanation of why two representations generally outperform one for time-irreversible processes. 6 pages, 4 figs 

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Toward an AI Physicist for Unsupervised Learning

Nov 05, 2018
Tailin Wu, Max Tegmark

* Typos fixed, references added, discussion improved. 18 pages, 7 figs 

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Meta-learning autoencoders for few-shot prediction

Jul 26, 2018
Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark

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The power of deeper networks for expressing natural functions

Apr 27, 2018
David Rolnick, Max Tegmark

* Replaced to match version published at ICLR 2018. 14 pages, 2 figs 

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Gated Orthogonal Recurrent Units: On Learning to Forget

Oct 25, 2017
Li Jing, Caglar Gulcehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljačić, Yoshua Bengio

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Criticality in Formal Languages and Statistical Physics

Aug 23, 2017
Henry W. Lin, Max Tegmark

* Entropy, 19, 299 (2017) 
* Replaced to match final published version. Discussion improved, references added 

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Why does deep and cheap learning work so well?

Aug 03, 2017
Henry W. Lin, Max Tegmark, David Rolnick

* Replaced to match version published in Journal of Statistical Physics: Improved refs & discussion, typos fixed. 16 pages, 3 figs 

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

Apr 03, 2017
Li Jing, Yichen Shen, Tena Dubček, John Peurifoy, Scott Skirlo, Yann LeCun, Max Tegmark, Marin Soljačić

* 9 pages, 4 figures 

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Research Priorities for Robust and Beneficial Artificial Intelligence

Feb 10, 2016
Stuart Russell, Daniel Dewey, Max Tegmark

* AI Magazine 36:4 (2015) 
* This article gives examples of the type of research advocated by the open letter for robust & beneficial AI at 

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Friendly Artificial Intelligence: the Physics Challenge

Sep 03, 2014
Max Tegmark

* In proceedings of the AAAI 2015 Workshop On AI and Ethics, p87, Toby Walsh, Ed. (2015) 
* 3 pages 

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The importance of quantum decoherence in brain processes

Nov 10, 1999
Max Tegmark

* Phys.Rev.E61:4194-4206,2000 
* Minor changes to match accepted PRE version. 15 pages with 5 figs included. Color figures and links at or from [email protected]. Physical Review E, in press 

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