Maximum Entropy Models: A Promising Link Between Statistical Physics, Inference, and Biology
FOCUS · F41 ·
Presentations
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Learning probabilities from random observables in high dimensions: the maximum entropy distribution and others
ORAL
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Authors
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Tomoyuki Obuchi
- Tokyo Institute of Technology
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Simona Cocco
- Laboratoire de Physique Statistique de l’Ecole Normale Superieure
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Remi Monasson
- Laboratoire de Physique Theorique de l’Ecole Normale Superieure
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Semiparametric energy-based models of systems exhibiting criticality
ORAL
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Authors
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Jan Humplik
- Institute of Science and Technology Austria
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Gasper Tkacik
- Institute of Science and Technology Austria
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From Maximum Entropy Models to Non-Stationarity and Irreversibility
ORAL
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Authors
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Rodrigo Cofre
- Department of Theoretical Physics, University of Geneva, Switzerland
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Bruno Cessac
- Inria, Neuromathcomp team
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Cesar Maldonado
- Centro de Modelamiento Matematico, Universidad de Chile
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Learning Maximal Entropy Models from finite size datasets: a fast Data-Driven algorithm allows to sample from the posterior distribution.
ORAL
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Authors
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Ulisse Ferrari
- Institut de la Vision, Sorbonne Universit\' es, UPMC, INSERM U968, CNRS, UMR\_ 7210, Paris, F-75012, France.
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UniEnt: uniform entropy model for the dynamics of a neuronal population
ORAL
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Authors
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Damian Hernandez Lahme
- Department of Physics, Emory University
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Ilya Nemenman
- Department of Physics, Emory University
- Departments of Physics and Biology, Emory University
- Emory Univ
- Emory University
- Department of Physics and Department of Biology, Emory University
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Modeling the Mass Action Dynamics of Metabolism with Fluctuation Theorems and Maximum Entropy.
ORAL
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Authors
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William Cannon
- Pacific Northwest National Laboratory
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Dennis Thomas
- Pacific Northwest National Laboratory
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Douglas Baxter
- Pacific Northwest National Laboratory
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Jeremy Zucker
- Pacific Northwest National Laboratory
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Garrett Goh
- Pacific Northwest National Laboratory
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On the sufficiency of pairwise interactions in maximum entropy models of networks
ORAL
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Authors
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Ilya Nemenman
- Department of Physics, Emory University
- Departments of Physics and Biology, Emory University
- Emory Univ
- Emory University
- Department of Physics and Department of Biology, Emory University
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Lina Merchan
- Savannah State University
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Insights in connecting phenotypes in bacteria to coevolutionary information.
ORAL
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Authors
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Ryan Cheng
- Rice University
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Faruck Morcos
- University of Texas at Dallas
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Ryan Hayes
- University of Michigan
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Rodney Helm
- University of Houston
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Herbert Levine
- Rice University
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Jose Onuchic
- Rice University
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Computational Amide I Spectroscopy for Refinement of Disordered Peptide Ensembles: Maximum Entropy and Related Approaches
ORAL
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Authors
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Michael Reppert
- Massachusetts Inst of Tech-MIT
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Andrei Tokmakoff
- The University of Chicago
- University of Chicago
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Coevolutionary modeling of protein sequences: Predicting structure, function, and mutational landscapes
COFFEE_KLATCH · Invited
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Authors
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Martin Weigt
- Universite Pierre et Marie Curie, Paris
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Phase transitions in Hidden Markov Models
ORAL
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Authors
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John Bechhoefer
- Simon Fraser Univ
- Simon Fraser University
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Emma Lathouwers
- Simon Fraser Univ
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Distinguishing cell type using epigenotype
ORAL
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Authors
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Thomas Wytock
- Dept. Physics and Astronomy, Northwestern University
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Adilson E Motter
- Northwestern Unviersity
- Northwestern University
- Department of Physics and Astronomy, Northwestern University
- Dept. Physics and Astronomy, Northwestern University
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Can simple interactions capture complex features of neural activity underlying behavior in a virtual reality environment?
ORAL
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Authors
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Leenoy Meshulam
- Princeton Univeristy
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Jeffrey Gauthier
- Princeton Univeristy
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Carlos Brody
- Princeton Univeristy
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David Tank
- Princeton Univeristy
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William Bialek
- Princeton Univeristy
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