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Rémi Monasson

Showing results (1-10 of 58) with videos related to

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Neural Computation|January 29, 2021
Low-Dimensional Manifolds Support Multiplexed Integrations in Recurrent Neural NetworksArnaud Fanthomme, Rémi Monasson
Physical Review. E|June 25, 2020
Spectrum of multispace Euclidean random matricesAldo Battista, Rémi Monasson
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|October 9, 2002
Exponentially hard problems are sometimes polynomial, a large deviation analysis of search algorithms for the random satisfiability problem, and its application to stop-and-restart resolutionsSimona Cocco, Rémi Monasson
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|March 5, 2004
Field-theoretic approach to metastability in the contact processChristophe Deroulers, Rémi Monasson
Physical Review Letters|February 15, 2020
Capacity-Resolution Trade-Off in the Optimal Learning of Multiple Low-Dimensional Manifolds by Attractor Neural NetworksAldo Battista, Rémi Monasson
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|October 26, 2005
Relaxation and metastability in a local search procedure for the random satisfiability problemGuilhem Semerjian, Rémi Monasson
Physical Review. E|January 20, 2024
Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivityTobias Kühn, Rémi Monasson
Physical Review. E|June 17, 2021
Survival probability and size of lineages in antibody affinity maturationMarco Molari, Rémi Monasson, Simona Cocco
Elife|March 13, 2019
Learning protein constitutive motifs from sequence dataJérôme Tubiana, Simona Cocco, Rémi Monasson
Proceedings of the National Academy of Sciences of the United States of America|June 18, 2024
Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation informationSimona Cocco, Lorenzo Posani, Rémi Monasson
Pageof 6

Showing results (1-10 of 58) with videos related to

Sort By:
Pageof 6
Neural Computation|January 29, 2021
Low-Dimensional Manifolds Support Multiplexed Integrations in Recurrent Neural NetworksArnaud Fanthomme, Rémi Monasson
Physical Review. E|June 25, 2020
Spectrum of multispace Euclidean random matricesAldo Battista, Rémi Monasson
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|October 9, 2002
Exponentially hard problems are sometimes polynomial, a large deviation analysis of search algorithms for the random satisfiability problem, and its application to stop-and-restart resolutionsSimona Cocco, Rémi Monasson
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|March 5, 2004
Field-theoretic approach to metastability in the contact processChristophe Deroulers, Rémi Monasson
Physical Review Letters|February 15, 2020
Capacity-Resolution Trade-Off in the Optimal Learning of Multiple Low-Dimensional Manifolds by Attractor Neural NetworksAldo Battista, Rémi Monasson
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|October 26, 2005
Relaxation and metastability in a local search procedure for the random satisfiability problemGuilhem Semerjian, Rémi Monasson
Physical Review. E|January 20, 2024
Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivityTobias Kühn, Rémi Monasson
Physical Review. E|June 17, 2021
Survival probability and size of lineages in antibody affinity maturationMarco Molari, Rémi Monasson, Simona Cocco
Elife|March 13, 2019
Learning protein constitutive motifs from sequence dataJérôme Tubiana, Simona Cocco, Rémi Monasson
Proceedings of the National Academy of Sciences of the United States of America|June 18, 2024
Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation informationSimona Cocco, Lorenzo Posani, Rémi Monasson
Pageof 6