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Lorenzo Giambagli

Showing results (1-10 of 8) with videos related to

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Physical Review Letters|May 19, 2023
Global Topological Synchronization on Simplicial and Cell ComplexesTimoteo Carletti, Lorenzo Giambagli, Ginestra Bianconi
Nature Communications|February 27, 2021
Machine learning in spectral domainLorenzo Giambagli, Lorenzo Buffoni, Timoteo Carletti, et al.
Scientific Reports|July 1, 2022
Spectral pruning of fully connected layersLorenzo Buffoni, Enrico Civitelli, Lorenzo Giambagli, et al.
Physical Review. E|January 21, 2023
Diffusion-driven instability of topological signals coupled by the Dirac operatorLorenzo Giambagli, Lucille Calmon, Riccardo Muolo, et al.
Physical Review. E|December 24, 2021
Training of sparse and dense deep neural networks: Fewer parameters, same performanceLorenzo Chicchi, Lorenzo Giambagli, Lorenzo Buffoni, et al.
Nature Communications|November 10, 2025
Peering inside the black box by learning the relevance of many-body functions in neural network potentialsKlara Bonneau, Jonas Lederer, Clark Templeton, et al.
Nature Communications|February 18, 2026
Extending the range of graph neural networks with global encodingsAlessandro Caruso, Jacopo Venturin, Lorenzo Giambagli, et al.
Neural Computation|March 3, 2025
Learning in Wilson-Cowan Model for MetapopulationRaffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Physical Review Letters|May 19, 2023
Global Topological Synchronization on Simplicial and Cell ComplexesTimoteo Carletti, Lorenzo Giambagli, Ginestra Bianconi
Nature Communications|February 27, 2021
Machine learning in spectral domainLorenzo Giambagli, Lorenzo Buffoni, Timoteo Carletti, et al.
Scientific Reports|July 1, 2022
Spectral pruning of fully connected layersLorenzo Buffoni, Enrico Civitelli, Lorenzo Giambagli, et al.
Physical Review. E|January 21, 2023
Diffusion-driven instability of topological signals coupled by the Dirac operatorLorenzo Giambagli, Lucille Calmon, Riccardo Muolo, et al.
Physical Review. E|December 24, 2021
Training of sparse and dense deep neural networks: Fewer parameters, same performanceLorenzo Chicchi, Lorenzo Giambagli, Lorenzo Buffoni, et al.
Nature Communications|November 10, 2025
Peering inside the black box by learning the relevance of many-body functions in neural network potentialsKlara Bonneau, Jonas Lederer, Clark Templeton, et al.
Nature Communications|February 18, 2026
Extending the range of graph neural networks with global encodingsAlessandro Caruso, Jacopo Venturin, Lorenzo Giambagli, et al.
Neural Computation|March 3, 2025
Learning in Wilson-Cowan Model for MetapopulationRaffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi, et al.
Pageof 1