From the 1 of 8 linked papers with an AI index.
8 papers
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…
A short review on the maximum clique problem algorithms with classical, AI, and quantum methods
Raffaele Marino, Lorenzo Buffoni, Bogdan Zavalnij
The paper surveys algorithms for solving the maximum clique problem, covering classical exact and heuristic methods as well as recent graph neural network and quantum computing app…
Approximating velocity fields with planted attractors via Neural-ODEs 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 attractors serve as indicat…
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…
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…
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…