Rémi Monasson
researcher
Facts
6 properties- Instance of
- human
- Occupation
- researcher
- Doctoral advisor
- Marc Mézard[2]
- Doctoral student
- Lorenzo Posani[4]
- On focus list of Wikimedia project
- WikiProject COVID-19[5]
🏷️ Also known as
en Rémi Monasson
🔖 Identifiers · 11
- ISNI
- 0000000003354403
- ORCID iD
- 0000-0002-4459-0204
- VIAF cluster ID
- 37164432
- OpenAlex ID
- A5016002177as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- Bibliothèque nationale de France ID
- 14594013r
- IdRef ID
- 081201257
- KBR person ID
- 13997161
- Mathematics Genealogy Project ID
- 303050
- NUKAT ID
- n2019031683
- Savoirs ENS ID
- 1728
- Yale LUX ID
- person/f280f320-9911-4d4c-8c12-ef286a59a1eb
📚 Sources · 1
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 17
Barriers and dynamical paths in alternating Gibbs sampling of restricted Boltzmann machines,
Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models,
Can Grid Cell Ensembles Represent Multiple Spaces?,
Capacity-Resolution Trade-Off in the Optimal Learning of Multiple Low-Dimensional Manifolds by Attractor Neural Networks,
Disentangling Representations in Restricted Boltzmann Machines without Adversaries,
Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information,
Functional networks from inverse modeling of neural population activity,
Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
+9 more
← Authored by 17
Barriers and dynamical paths in alternating Gibbs sampling of restricted Boltzmann machines,
Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models,
Can Grid Cell Ensembles Represent Multiple Spaces?,
Capacity-Resolution Trade-Off in the Optimal Learning of Multiple Low-Dimensional Manifolds by Attractor Neural Networks,
Disentangling Representations in Restricted Boltzmann Machines without Adversaries,
Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information,
Functional networks from inverse modeling of neural population activity,
Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
+9 more
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