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Francesco Zamponi

Showing results (41-50 of 87) with videos related to

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Physical Review. E|November 18, 2025
Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics modelsLuca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi
Soft Matter|November 24, 2015
Spontaneous instabilities and stick-slip motion in a generalized Hébraud-Lequeux modelJean-Philippe Bouchaud, Stanislao Gualdi, Marco Tarzia, et al.
Physical Review. E|January 20, 2021
Aligning biological sequences by exploiting residue conservation and coevolutionAnna Paola Muntoni, Andrea Pagnani, Martin Weigt, et al.
BMC Bioinformatics|October 30, 2021
adabmDCA: adaptive Boltzmann machine learning for biological sequencesAnna Paola Muntoni, Andrea Pagnani, Martin Weigt, et al.
Plos Computational Biology|March 16, 2026
Functional bottlenecks can emerge from non-epistatic underlying traitsAnna Ottavia Schulte, Samar Alqatari, Saverio Rossi, et al.
Proceedings of the National Academy of Sciences of the United States of America|May 8, 2026
Demonstrating real advantage of machine learning-enhanced Monte Carlo for combinatorial optimizationLuca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi
Molecular Biology and Evolution|November 9, 2021
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein EvolutionMatteo Bisardi, Juan Rodriguez-Rivas, Francesco Zamponi, et al.
Reports on Progress in Physics. Physical Society (Great Britain)|July 1, 2025
Fluctuations and the limit of predictability in protein evolutionSaverio Rossi, Leonardo Di Bari, Martin Weigt, et al.
The Journal of Chemical Physics|March 16, 2022
Supervised perceptron learning vs unsupervised Hebbian unlearning: Approaching optimal memory retrieval in Hopfield-like networksMarco Benedetti, Enrico Ventura, Enzo Marinari, et al.
Nature Communications|April 25, 2014
Fractal free energy landscapes in structural glassesPatrick Charbonneau, Jorge Kurchan, Giorgio Parisi, et al.
Pageof 9

Showing results (41-50 of 87) with videos related to

Sort By:
Pageof 9
Physical Review. E|November 18, 2025
Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics modelsLuca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi
Soft Matter|November 24, 2015
Spontaneous instabilities and stick-slip motion in a generalized Hébraud-Lequeux modelJean-Philippe Bouchaud, Stanislao Gualdi, Marco Tarzia, et al.
Physical Review. E|January 20, 2021
Aligning biological sequences by exploiting residue conservation and coevolutionAnna Paola Muntoni, Andrea Pagnani, Martin Weigt, et al.
BMC Bioinformatics|October 30, 2021
adabmDCA: adaptive Boltzmann machine learning for biological sequencesAnna Paola Muntoni, Andrea Pagnani, Martin Weigt, et al.
Plos Computational Biology|March 16, 2026
Functional bottlenecks can emerge from non-epistatic underlying traitsAnna Ottavia Schulte, Samar Alqatari, Saverio Rossi, et al.
Proceedings of the National Academy of Sciences of the United States of America|May 8, 2026
Demonstrating real advantage of machine learning-enhanced Monte Carlo for combinatorial optimizationLuca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi
Molecular Biology and Evolution|November 9, 2021
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein EvolutionMatteo Bisardi, Juan Rodriguez-Rivas, Francesco Zamponi, et al.
Reports on Progress in Physics. Physical Society (Great Britain)|July 1, 2025
Fluctuations and the limit of predictability in protein evolutionSaverio Rossi, Leonardo Di Bari, Martin Weigt, et al.
The Journal of Chemical Physics|March 16, 2022
Supervised perceptron learning vs unsupervised Hebbian unlearning: Approaching optimal memory retrieval in Hopfield-like networksMarco Benedetti, Enrico Ventura, Enzo Marinari, et al.
Nature Communications|April 25, 2014
Fractal free energy landscapes in structural glassesPatrick Charbonneau, Jorge Kurchan, Giorgio Parisi, et al.
Pageof 9