36 citations · 66 across the 12 of their papers we have counts for
13 papers
Data geometry preserves prediction but reshapes explanation
Nicola Amoroso, Mario Caruso, Marianna La Rocca +8
Across scientific domains, empirical data often give rise to positive semidefinite matrices that encode similarities, couplings or interactions and induce natural geometries. We in…
Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations
Domenico Pomarico, Alessandra Costantino, Gabriel Ramirez Sanchez +12
A quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced, leveraging AlphaEarth embeddings and Matrix Product Stat…
Transfer entropy and O-information to detect grokking in tensor network multi-class classification problems
Domenico Pomarico, Roberto Cilli, Alfonso Monaco +10
Quantum-enhanced machine learning, encompassing both quantum algorithms and quantum-inspired classical methods such as tensor networks, offers promising tools for extracting struct…
Grokking as an entanglement transition in tensor network machine learning
Domenico Pomarico, Alfonso Monaco, Giuseppe Magnifico +11
Grokking is a intriguing phenomenon in machine learning where a neural network, after many training iterations with negligible improvement in generalization, suddenly achieves high…
Mind the gap in university rankings: a complex network approach towards fairness
Loredana Bellantuono, Alfonso Monaco, Nicola Amoroso +6
University rankings are increasingly adopted for academic comparison and success quantification, even to establish performance-based criteria for funding assignment. However, ranki…
Potential energy of complex networks: a novel perspective
Nicola Amoroso, Loredana Bellantuono, Saverio Pascazio +4
We present a novel characterization of complex networks, based on the potential of an associated Schrödinger equation. The potential is designed so that the energy spectrum of the…