Search Results for author: Pedro A. M. Mediano

Found 11 papers, 4 papers with code

Greater than the parts: A review of the information decomposition approach to causal emergence

no code implementations12 Nov 2021 Pedro A. M. Mediano, Fernando E. Rosas, Andrea I. Luppi, Henrik J. Jensen, Anil K. Seth, Adam B. Barrett, Robin L. Carhart-Harris, Daniel Bor

Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons.

Towards an extended taxonomy of information dynamics via Integrated Information Decomposition

no code implementations27 Sep 2021 Pedro A. M. Mediano, Fernando E. Rosas, Andrea I Luppi, Robin L. Carhart-Harris, Daniel Bor, Anil K. Seth, Adam B. Barrett

Complex systems, from the human brain to the global economy, are made of multiple elements that interact in such ways that the behaviour of the `whole' often seems to be more than what is readily explainable in terms of the `sum of the parts.'

Causal Discovery

Integrated information as a common signature of dynamical and information-processing complexity

no code implementations18 Jun 2021 Pedro A. M. Mediano, Fernando E. Rosas, Juan Carlos Farah, Murray Shanahan, Daniel Bor, Adam B. Barrett

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress.

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

no code implementations14 Oct 2020 Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar

Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas.

General Classification

Causal blankets: Theory and algorithmic framework

no code implementations28 Aug 2020 Fernando E. Rosas, Pedro A. M. Mediano, Martin Biehl, Shamil Chandaria, Daniel Polani

We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics.

Deep active inference agents using Monte-Carlo methods

1 code implementation NeurIPS 2020 Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, Karl Friston

In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i. e., plan), to evince reward-directed navigation - despite temporary suspension of visual input.

Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data

1 code implementation17 Apr 2020 Fernando E. Rosas, Pedro A. M. Mediano, Henrik J. Jensen, Anil. K. Seth, Adam B. Barrett, Robin L. Carhart-Harris, Daniel Bor

The broad concept of emergence is instrumental in various of the most challenging open scientific questions -- yet, few quantitative theories of what constitutes emergent phenomena have been proposed.

Beyond integrated information: A taxonomy of information dynamics phenomena

1 code implementation5 Sep 2019 Pedro A. M. Mediano, Fernando Rosas, Robin L. Carhart-Harris, Anil. K. Seth, Adam B. Barrett

Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise -- every 'cause' variable has an associated 'effect' variable, so that a 'causal arrow' can be drawn between them.

Neurons and Cognition Data Analysis, Statistics and Probability

Relational Forward Models for Multi-Agent Learning

no code implementations ICLR 2019 Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia

The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them.

Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality

no code implementations5 Jul 2017 Xerxes D. Arsiwalla, Pedro A. M. Mediano, Paul F. M. J. Verschure

Recent complexity measures such as integrated information have sought to operationalize this problem taking a whole-versus-parts perspective, wherein one explicitly computes the amount of information generated by a network as a whole over and above that generated by the sum of its parts during state transitions.

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

3 code implementations8 Nov 2016 Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C. H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan

We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models.

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