activity
20232026
most citedCharging a quantum spin network towards Heisenberg-limited precision

3 citations · 5 across the 14 of their papers we have counts for

collaborators
Showing 2026Show all

6 papers · 1 filter

cs.LG2026

Learning-Based Reconstruction of Optical Properties in Bilayered Media from Single-distance Time-Resolved Reflectance Measurements

Caterina Amendola, Giulia Maffeis, Lorenzo Buffoni +8

The inverse problem of reconstructing optical properties, specifically absorption and scattering coefficients, in layered biological media from time-domain reflectance measurements…

cond-mat.dis-nn2026

Multiplicity of Stable Attractors in Disordered Neural Models

Raffaele Marino, Roberto Livi, Antonio Politi

We show how large-deviation statistics allows one to obtain reliable estimates of the multiplicity of stable fixed-points in a model of neural ordinary differential equations previ…

cond-mat.dis-nn2026

Approximating velocity fields with planted point attractors via Neural-ODE for classification purposes

Feliciano Giuseppe Pacifico, Duccio Fanelli, Lorenzo Buffoni +3

In this work, Neural ODEs equipped with a curated collection of equilibrium points have been successfully employed for classification tasks. The planted fixed points serve as indic…

cond-mat.dis-nn2026

Exact Fixed-Point Constraints in Neural-ODEs with Provable Universality

Feliciano Giuseppe Pacifico, Duccio Fanelli, Lorenzo Buffoni +3

We introduce a technique that enables Neural-ODEs to approximate arbitrary velocity fields with a priori planted fixed-points. Specifically, a recipe is given to explicitly accommo…

cond-mat.dis-nn2026

Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems

Geri Skenderi, Lorenzo Buffoni, Francesco D'Amico +6

Graph neural networks (GNNs) are increasingly applied to hard optimization problems, often claiming superiority over classical heuristics. However, such claims risk being unsolid d…

cond-mat.stat-mech2026

Smart Walkers in Discrete Space

Gianluca Peri, Lorenzo Buffoni, Giacomo Chiti +4

We study the statistical properties of trainable agents moving in discrete space. After introducing the mathematical framework, we first analyze the dynamics of two completely rand…