15 citations · 17 across the 7 of their papers we have counts for
8 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…
Multi-scale Poisson process approaches for differential expression analysis of high-throughput sequencing data
Heejung Shim, Zhengrong Xing, Ester Pantaleo +3
Estimating and testing for differences in molecular phenotypes (e.g. gene expression, chromatin accessibility, transcription factor binding) across conditions is an important part…
When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators
Ester Pantaleo, Michele Tumminello, Fabrizio Lillo +1
The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empiric…
Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing
Robert B. Gramacy, Ester Pantaleo
Portfolio balancing requires estimates of covariance between asset returns. Returns data have histories which greatly vary in length, since assets begin public trading at different…
Hausdorff clustering
N. Basalto, R. Bellotti, F. De Carlo +3
A clustering algorithm based on the Hausdorff distance is introduced and compared to the single and complete linkage. The three clustering procedures are applied to a toy example a…