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CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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2020

Udrescu, S. - M. et al. AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020).

2006.10782.pdf (2.62 MB)

2017

Jing, L. et al. Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. 34th International Conference on Machine Learning 70, 1733-1741 (2017).

1612.05231.pdf (2.3 MB)

Lin, H. & Tegmark, M. Why does deep and cheap learning work so well?. Journal of Statistical Physics 168, 1223–1247 (2017).

1608.08225.pdf (2.14 MB)

2016

Lin, H. & Tegmark, M. Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language. arXiv.org (2016).

Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language (1.64 MB)

Tegmark, M. Improved Measures of Integrated Information. PLOS Computational Biology (2016). doi:10.1371/journal.pcbi.100512310.1371

1601.02626.pdf (3.49 MB)