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- Max Planck SocietyDE50 papers
- Max Planck Institute for Intelligent SystemsDE30 papers
- University of TübingenDE28 papers
- Bernstein Center for Computational Neuroscience TübingenDE7 papers
- California Institute of TechnologyUS5 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- Max Planck Institute for Dynamics and Self-OrganizationDE5 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS4 papers
- École Polytechnique Fédérale de LausanneCH4 papers
- ETH ZurichCH4 papers
- Harvard UniversityUS4 papers
- Otto-von-Guericke-Universität MagdeburgDE4 papers
6 papers · 1 filter
Self-organization toward criticality by synaptic plasticity
Roxana Zeraati, Viola Priesemann, Anna Levina
Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such sca…
Causal learning with sufficient statistics: an information bottleneck approach
Daniel Chicharro, Michel Besserve, Stefano Panzeri
The inference of causal relationships using observational data from partially observed multivariate systems with hidden variables is a fundamental question in many scientific domai…
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA
Hugo Richard, Luigi Gresele, Aapo Hyvärinen +3
Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. However, the aggregation of data coming from multip…
A theory of independent mechanisms for extrapolation in generative models
Michel Besserve, Rémy Sun, Dominik Janzing +1
Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea…
Static and Dynamic Values of Computation in MCTS
Eren Sezener, Peter Dayan
Monte-Carlo Tree Search (MCTS) is one of the most-widely used methods for planning, and has powered many recent advances in artificial intelligence. In MCTS, one typically performs…
MRzero -- Fully automated discovery of MRI sequences using supervised learning
Alexander Loktyushin, Kai Herz, Nam Dang +7
Purpose: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with…