10 citations · 17 across the 10 of their papers we have counts for
11 papers
A Flatness-Generalization Relation in the Teacher-Student Tree-Committee Machine
Brandon Livio Annesi, Davide Straziota, Enrico Maria Malatesta
The flatness of the loss landscape at a minimizer is a widely used heuristic for reasoning about neural-network generalization, yet evidence for this relation is mostly empirical a…
On the robustness of noisy solutions in non-convex neural networks
Enrico M. Malatesta, Alessandra Passalacqua, Riccardo Zecchina
Optimization in non-convex neural network models is strongly influenced by the geometry of the solution space: sparse, isolated, point-like clusters are typically algorithmically i…
Collision Resistance of Single-Layer Neural Nets
Marco Benedetti, Andrej Bogdanov, Enrico M. Malatesta +5
We initiate the study of the algorithmic complexity of finding collisions in single-layer binary neural networks. Given a random matrix , an…
Are Neural Networks Collision Resistant?
Marco Benedetti, Andrej Bogdanov, Enrico M. Malatesta +5
When neural networks are trained to classify a dataset, one finds a set of weights from which the network produces a label for each data point. We study the algorithmic complexity…
Overlap Gap and Computational Thresholds in the Square Wave Perceptron
Marco Benedetti, Andrej Bogdanov, Enrico M. Malatesta +5
Square Wave Perceptrons (SWPs) form a class of neural network models with oscillating activation function that exhibit intriguing ``hardness'' properties in the high-dimensional li…
Rare Trajectories in a Prototypical Mean-field Disordered Model: Insights into Landscape and Instantons
Patrick Charbonneau, Giampaolo Folena, Enrico M. Malatesta +2
For disordered systems within the random first-order transition (RFOT) universality class, such as structural glasses and certain spin glasses, the role played by activated relaxat…
Sampling the space of solutions of an artificial neural network
Alessandro Zambon, Enrico M. Malatesta, Guido Tiana +1
The weight space of an artificial neural network can be systematically explored using tools from statistical mechanics. We employ a combination of a hybrid Monte Carlo algorithm wh…
Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks
Brandon L. Annesi, Enrico M. Malatesta, Francesco Zamponi
We analyze the problem of storing random pattern-label associations using two classes of continuous non-convex weights models, namely the perceptron with negative margin and an inf…
Impact of dendritic non-linearities on the computational capabilities of neurons
Clarissa Lauditi, Enrico M. Malatesta, Fabrizio Pittorino +3
How neurons integrate the myriad synaptic inputs scattered across their dendrites is a fundamental question in neuroscience. Multiple neurophysiological experiments have shown that…
The twin peaks of learning neural networks
Elizaveta Demyanenko, Christoph Feinauer, Enrico M. Malatesta +1
Recent works demonstrated the existence of a double-descent phenomenon for the generalization error of neural networks, where highly overparameterized models escape overfitting and…
High-dimensional manifold of solutions in neural networks: insights from statistical physics
Enrico M. Malatesta
In these pedagogic notes I review the statistical mechanics approach to neural networks, focusing on the paradigmatic example of the perceptron architecture with binary an continuo…