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2020 – today
- 2022
[i26]Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Max Tegmark, Isaac L. Chuang:
Biological error correction codes generate fault-tolerant neural networks. CoRR abs/2202.12887 (2022)- [i25]Ziming Liu, Varun Madhavan, Max Tegmark:
AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations. CoRR abs/2203.12610 (2022) - [i24]Andrew K. Tan, Max Tegmark, Isaac L. Chuang:
Pareto-optimal clustering with the primal deterministic information bottleneck. CoRR abs/2204.02489 (2022) - 2021
- [i23]Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu:
Machine-Learning Non-Conservative Dynamics for New-Physics Detection. CoRR abs/2106.00026 (2021) - [i22]Samantha D'Alonzo, Max Tegmark:
Machine-Learning media bias. CoRR abs/2109.00024 (2021) - [i21]Ziming Liu, Max Tegmark:
Machine-learning hidden symmetries. CoRR abs/2109.09721 (2021) - [i20]Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark:
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning. CoRR abs/2109.13901 (2021) - 2020
- [j8]Max Tegmark
, Tailin Wu:
Pareto-Optimal Data Compression for Binary Classification Tasks. Entropy 22(1): 7 (2020) - [c6]Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark:
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. NeurIPS 2020 - [i19]Silviu-Marian Udrescu, Max Tegmark:
Symbolic Pregression: Discovering Physical Laws from Raw Distorted Video. CoRR abs/2005.11212 (2020) - [i18]Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark:
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. CoRR abs/2006.10782 (2020) - [i17]Ziming Liu, Max Tegmark:
AI Poincaré: Machine Learning Conservation Laws from Trajectories. CoRR abs/2011.04698 (2020)
2010 – 2019
- 2019
- [j7]Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark:
Learnability for the Information Bottleneck. Entropy 21(10): 924 (2019) - [j6]Li Jing, Çaglar Gülçehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio:
Gated Orthogonal Recurrent Units: On Learning to Forget. Neural Comput. 31(4) (2019) - [c5]Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark:
Learnability for the Information Bottleneck. UAI 2019: 1050-1060 - [i16]Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, Simone Langhans, Max Tegmark, Francesco Fuso Nerini:
The role of artificial intelligence in achieving the Sustainable Development Goals. CoRR abs/1905.00501 (2019) - [i15]Silviu-Marian Udrescu, Max Tegmark:
AI Feynman: a Physics-Inspired Method for Symbolic Regression. CoRR abs/1905.11481 (2019) - [i14]Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark:
Learnability for the Information Bottleneck. CoRR abs/1907.07331 (2019) - [i13]Max Tegmark, Tailin Wu:
Pareto-optimal data compression for binary classification tasks. CoRR abs/1908.08961 (2019) - 2018
- [c4]Li Jing, Çaglar Gülçehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio:
Gated Orthogonal Recurrent Units: On Learning to Forget. AAAI Workshops 2018: 720-726 - [c3]David Rolnick, Max Tegmark:
The power of deeper networks for expressing natural functions. ICLR (Poster) 2018 - [i12]Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark:
Meta-learning autoencoders for few-shot prediction. CoRR abs/1807.09912 (2018) - [i11]Tailin Wu, Max Tegmark:
Toward an AI Physicist for Unsupervised Learning. CoRR abs/1810.10525 (2018) - 2017
- [j5]Henry W. Lin, Max Tegmark:
Critical Behavior in Physics and Probabilistic Formal Languages. Entropy 19(7): 299 (2017) - [c2]Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic:
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs. ICML 2017: 1733-1741 - [i10]Ben Garfinkel, Miles Brundage, Daniel Filan, Carrick Flynn, Jelena Luketina, Michael Page, Anders Sandberg, Andrew Snyder-Beattie, Max Tegmark:
On the Impossibility of Supersized Machines. CoRR abs/1703.10987 (2017) - [i9]David Rolnick, Max Tegmark:
The power of deeper networks for expressing natural functions. CoRR abs/1705.05502 (2017) - [i8]Li Jing, Çaglar Gülçehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljacic, Yoshua Bengio:
Gated Orthogonal Recurrent Units: On Learning to Forget. CoRR abs/1706.02761 (2017) - 2016
- [j4]Max Tegmark:
Improved Measures of Integrated Information. PLoS Comput. Biol. 12(11) (2016) - [i7]Stuart Russell, Daniel Dewey, Max Tegmark:
Research Priorities for Robust and Beneficial Artificial Intelligence. CoRR abs/1602.03506 (2016) - [i6]Henry Lin, Max Tegmark:
Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language. CoRR abs/1606.06737 (2016) - [i5]Henry W. Lin, Max Tegmark:
Why does deep and cheap learning work so well? CoRR abs/1608.08225 (2016) - [i4]Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Max Tegmark, Marin Soljacic:
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. CoRR abs/1612.05231 (2016) - 2015
- [j3]Stuart Russell, Daniel Dewey, Max Tegmark:
Research Priorities for Robust and Beneficial Artificial Intelligence. AI Mag. 36(4): 105-114 (2015) - [c1]Max Tegmark:
Friendly Artificial Intelligence: The Physics Challenge. AAAI Workshop: AI and Ethics 2015 - [i3]Max Tegmark:
Nuclear War from a Cosmic Perspective. CoRR abs/1505.00246 (2015) - 2014
- [i2]Max Tegmark:
Friendly Artificial Intelligence: the Physics Challenge. CoRR abs/1409.0813 (2014)
2000 – 2009
- 2000
- [j2]Max Tegmark:
Why the brain is probably not a quantum computer. Inf. Sci. 128(3-4): 155-179 (2000)
1990 – 1999
- 1999
- [i1]Max Tegmark:
The importance of quantum decoherence in brain processes. CoRR quant-ph/9907009 (1999) - 1994
- [j1]Harold S. Shapiro, Max Tegmark:
An Elementary Proof That the Biharmonic Green Function of an Eccentric Ellipse Changes Sign. SIAM Rev. 36(1): 99-101 (1994)
Coauthor Index
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