Pedro A. M. Mediano
University of Cambridge
121 Papers
123 Citations
Pedro A. M. Mediano is an academic researcher from University of Cambridge. The author has contributed to research in topics: Computer science & Consciousness. The author has an hindex of 17, co-authored 52 publications. Previous affiliations of Pedro A. M. Mediano include Imperial College London & Queen Mary University of London.
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Papers
•Posted Content
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat Dilokthanakul,Pedro A. M. Mediano,Marta Garnelo,Matthew C. H. Lee,Hugh Salimbeni,Kai Arulkumaran,Murray Shanahan +6 more
TL;DR: It is shown that a heuristic called minimum information constraint that has been shown to mitigate this effect in VAEs can also be applied to improve unsupervised clustering performance with this variant of the variational autoencoder model with a Gaussian mixture as a prior distribution.
Quantifying high-order interdependencies via multivariate extensions of the mutual information
Fernando Rosas,Pedro A. M. Mediano,Michael Gastpar,Henrik Jeldtoft Jensen,Henrik Jeldtoft Jensen +4 more
TL;DR: A model-agnostic approach to study statistical synergy, a form of emergence in which patterns at large scales are not traceable from lower scales, and introduces the O-information as a metric that is capable of characterizing synergy- and redundancy-dominated systems.
Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data
Fernando Rosas,Pedro A. M. Mediano,Henrik Jeldtoft Jensen,Henrik Jeldtoft Jensen,Anil K. Seth,Anil K. Seth,Adam B. Barrett,Robin L. Carhart-Harris,Daniel Bor +8 more
TL;DR: The theory provides a quantitative definition of downward causation, and introduces a complementary modality of emergent behaviour—which the author refers to as causal decoupling—which allows practical criteria that can be efficiently calculated in large systems.
134
Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation
TL;DR: In this paper, the authors provide clear and intuitive descriptions of six distinct candidate measures of integrated information and explore the properties of each of these measures in simulation on networks consisting of eight interacting nodes, animated with Gaussian linear autoregressive dynamics.
121
A synergistic core for human brain evolution and cognition
Andrea I. Luppi,Pedro A. M. Mediano,Fernando Rosas,Negin Holland,T Fryer,John T. O'Brien,John T. O'Brien,James B. Rowe,James B. Rowe,James B. Rowe,David K. Menon,Daniel Bor,Emmanuel Stamatakis +12 more
TL;DR: In this article, the authors introduce a powerful framework to identify synergistic and redundant contributions to neural information processing and cognition, and reveal that synergistic interactions are the fundamental drivers of complex human cognition.