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Learning, compression, and leakage: Minimizing classification error via meta-universal compression principles

Oct 14, 2020
Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar

* 8 pages, no figures 

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Causal blankets: Theory and algorithmic framework

Sep 29, 2020
Fernando E. Rosas, Pedro A. M. Mediano, Martin Biehl, Shamil Chandaria, Daniel Polani

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Deep active inference agents using Monte-Carlo methods

Jun 07, 2020
Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, Karl Friston

* 16 pages, 8 figures 

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Relational Forward Models for Multi-Agent Learning

Sep 28, 2018
Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia

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Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality

Jul 05, 2017
Xerxes D. Arsiwalla, Pedro A. M. Mediano, Paul F. M. J. Verschure

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Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

Jan 13, 2017
Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C. H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan

* 12 pages, 6 figures, Under review as a conference paper at ICLR 2017 

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