4 citations · 4 across the 3 of their papers we have counts for
4 papers
Random Features Hopfield Networks generalize retrieval to previously unseen examples
Silvio Kalaj, Clarissa Lauditi, Gabriele Perugini +3
It has been recently shown that a learning transition happens when a Hopfield Network stores examples generated as superpositions of random features, where new attractors correspon…
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…
Storage and Learning phase transitions in the Random-Features Hopfield Model
Matteo Negri, Clarissa Lauditi, Gabriele Perugini +2
The Hopfield model is a paradigmatic model of neural networks that has been analyzed for many decades in the statistical physics, neuroscience, and machine learning communities. In…
Correlation Functions of the Anharmonic Oscillator: Numerical Verification of Two-Loop Corrections to the Large-Order Behavior
Ludovico T. Giorgini, Ulrich D. Jentschura, Enrico M. Malatesta +3
Recently, the large-order behavior of correlation functions of the -anharmonic oscillator has been analyzed by us in [L. T. Giorgini et el., Phys. Rev. D 101, 125001 (2020)].…