58 citations · 133 across the 9 of their papers we have counts for
9 papers
Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions
Fabiola Ricci, Lorenzo Bardone, Sebastian Goldt
Deep neural networks learn structured features from complex, non-Gaussian inputs, but the mechanisms behind this process remain poorly understood. Our work is motivated by the obse…
A distributional simplicity bias in the learning dynamics of transformers
Riccardo Rende, Federica Gerace, Alessandro Laio +1
The remarkable capability of over-parameterised neural networks to generalise effectively has been explained by invoking a ``simplicity bias'': neural networks prevent overfitting…
Sliding down the stairs: how correlated latent variables accelerate learning with neural networks
Lorenzo Bardone, Sebastian Goldt
Neural networks extract features from data using stochastic gradient descent (SGD). In particular, higher-order input cumulants (HOCs) are crucial for their performance. However, e…
Fine-tuning Neural Network Quantum States
Riccardo Rende, Sebastian Goldt, Federico Becca +1
Recent progress in the design and optimization of neural-network quantum states (NQSs) has made them an effective method to investigate ground-state properties of quantum many-body…
Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks
Eszter Székely, Lorenzo Bardone, Federica Gerace +1
Neural networks excel at discovering statistical patterns in high-dimensional data sets. In practice, higher-order cumulants, which quantify the non-Gaussian correlations between t…
Transformer Wave Function for two dimensional frustrated magnets: emergence of a Spin-Liquid Phase in the Shastry-Sutherland Model
Luciano Loris Viteritti, Riccardo Rende, Alberto Parola +2
Understanding quantum magnetism in two-dimensional systems represents a lively branch in modern condensed-matter physics. In the presence of competing super-exchange couplings, mag…
A simple linear algebra identity to optimize Large-Scale Neural Network Quantum States
Riccardo Rende, Luciano Loris Viteritti, Lorenzo Bardone +2
Neural-network architectures have been increasingly used to represent quantum many-body wave functions. These networks require a large number of variational parameters and are chal…
Mapping of attention mechanisms to a generalized Potts model
Riccardo Rende, Federica Gerace, Alessandro Laio +1
Transformers are neural networks that revolutionized natural language processing and machine learning. They process sequences of inputs, like words, using a mechanism called self-a…
Redundant representations help generalization in wide neural networks
Diego Doimo, Aldo Glielmo, Sebastian Goldt +1
Deep neural networks (DNNs) defy the classical bias-variance trade-off: adding parameters to a DNN that interpolates its training data will typically improve its generalization per…