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Giulio Biroli
Publication Activity (10 Years)
Years Active: 1999-2022
Publications (10 Years): 32
Top Topics
Convex Optimization
Neural Network
Hinge Loss
Empirical Risk
Top Venues
CoRR
NeurIPS
ICML
MSML
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Publications
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Stéphane d'Ascoli
,
Maria Refinetti
,
Giulio Biroli
Optimal learning rate schedules in high-dimensional non-convex optimization problems.
CoRR
(2022)
Franco Pellegrini
,
Giulio Biroli
Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks.
ICML
(2022)
Tanguy Marchand
,
Misaki Ozawa
,
Giulio Biroli
,
Stéphane Mallat
Wavelet Conditional Renormalization Group.
CoRR
(2022)
Stéphane d'Ascoli
,
Hugo Touvron
,
Matthew L. Leavitt
,
Ari S. Morcos
,
Giulio Biroli
,
Levent Sagun
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases.
ICML
(2021)
Stéphane d'Ascoli
,
Marylou Gabrié
,
Levent Sagun
,
Giulio Biroli
On the interplay between data structure and loss function in classification problems.
NeurIPS
(2021)
Stéphane d'Ascoli
,
Hugo Touvron
,
Matthew L. Leavitt
,
Ari S. Morcos
,
Giulio Biroli
,
Levent Sagun
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases.
CoRR
(2021)
Stéphane d'Ascoli
,
Levent Sagun
,
Giulio Biroli
,
Ari Morcos
Transformed CNNs: recasting pre-trained convolutional layers with self-attention.
CoRR
(2021)
Stéphane d'Ascoli
,
Marylou Gabrié
,
Levent Sagun
,
Giulio Biroli
More data or more parameters? Investigating the effect of data structure on generalization.
CoRR
(2021)
Franco Pellegrini
,
Giulio Biroli
Sifting out the features by pruning: Are convolutional networks the winning lottery ticket of fully connected ones?
CoRR
(2021)
Franco Pellegrini
,
Giulio Biroli
An analytic theory of shallow networks dynamics for hinge loss classification.
NeurIPS
(2020)
Felix Roy
,
Matthieu Barbier
,
Giulio Biroli
,
Guy Bunin
Complex interactions can create persistent fluctuations in high-diversity ecosystems.
PLoS Comput. Biol.
16 (5) (2020)
Stefano Sarao Mannelli
,
Giulio Biroli
,
Chiara Cammarota
,
Florent Krzakala
,
Pierfrancesco Urbani
,
Lenka Zdeborová
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval.
NeurIPS
(2020)
Stéphane d'Ascoli
,
Maria Refinetti
,
Giulio Biroli
,
Florent Krzakala
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime.
CoRR
(2020)
Antoine Maillard
,
Gérard Ben Arous
,
Giulio Biroli
Landscape Complexity for the Empirical Risk of Generalized Linear Models.
MSML
(2020)
Stefano Sarao Mannelli
,
Giulio Biroli
,
Chiara Cammarota
,
Florent Krzakala
,
Pierfrancesco Urbani
,
Lenka Zdeborová
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval.
CoRR
(2020)
Stéphane d'Ascoli
,
Levent Sagun
,
Giulio Biroli
Triple descent and the two kinds of overfitting: Where & why do they appear?
CoRR
(2020)
Franco Pellegrini
,
Giulio Biroli
An analytic theory of shallow networks dynamics for hinge loss classification.
CoRR
(2020)
Stéphane d'Ascoli
,
Maria Refinetti
,
Giulio Biroli
,
Florent Krzakala
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime.
ICML
(2020)
Stéphane d'Ascoli
,
Levent Sagun
,
Giulio Biroli
Triple descent and the two kinds of overfitting: where & why do they appear?
NeurIPS
(2020)
Antoine Maillard
,
Gérard Ben Arous
,
Giulio Biroli
Landscape Complexity for the Empirical Risk of Generalized Linear Models.
CoRR
(2019)
Stefano Sarao Mannelli
,
Giulio Biroli
,
Chiara Cammarota
,
Florent Krzakala
,
Lenka Zdeborová
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models.
NeurIPS
(2019)
Mario Geiger
,
Arthur Jacot
,
Stefano Spigler
,
Franck Gabriel
,
Levent Sagun
,
Stéphane d'Ascoli
,
Giulio Biroli
,
Clément Hongler
,
Matthieu Wyart
Scaling description of generalization with number of parameters in deep learning.
CoRR
(2019)
Giulio Biroli
,
Chiara Cammarota
,
Federico Ricci-Tersenghi
How to iron out rough landscapes and get optimal performances: Replicated Gradient Descent and its application to tensor PCA.
CoRR
(2019)
Stéphane d'Ascoli
,
Levent Sagun
,
Joan Bruna
,
Giulio Biroli
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias.
CoRR
(2019)
Stéphane d'Ascoli
,
Levent Sagun
,
Giulio Biroli
,
Joan Bruna
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias.
NeurIPS
(2019)
Stefano Sarao Mannelli
,
Giulio Biroli
,
Chiara Cammarota
,
Florent Krzakala
,
Lenka Zdeborová
Who is Afraid of Big Bad Minima? Analysis of Gradient-Flow in a Spiked Matrix-Tensor Model.
CoRR
(2019)
François P. Landes
,
Giulio Biroli
,
Olivier Dauchot
,
Andrea J. Liu
,
David R. Reichman
Attractive vs. truncated repulsive supercooled liquids : dynamics is encoded in the pair correlation function.
CoRR
(2019)
Stefano Spigler
,
Mario Geiger
,
Stéphane d'Ascoli
,
Levent Sagun
,
Giulio Biroli
,
Matthieu Wyart
A jamming transition from under- to over-parametrization affects loss landscape and generalization.
CoRR
(2018)
Marco Baity-Jesi
,
Levent Sagun
,
Mario Geiger
,
Stefano Spigler
,
Gérard Ben Arous
,
Chiara Cammarota
,
Yann LeCun
,
Matthieu Wyart
,
Giulio Biroli
Comparing Dynamics: Deep Neural Networks versus Glassy Systems.
CoRR
(2018)
Mario Geiger
,
Stefano Spigler
,
Stéphane d'Ascoli
,
Levent Sagun
,
Marco Baity-Jesi
,
Giulio Biroli
,
Matthieu Wyart
The jamming transition as a paradigm to understand the loss landscape of deep neural networks.
CoRR
(2018)
Marco Baity-Jesi
,
Levent Sagun
,
Mario Geiger
,
Stefano Spigler
,
Gérard Ben Arous
,
Chiara Cammarota
,
Yann LeCun
,
Matthieu Wyart
,
Giulio Biroli
Comparing Dynamics: Deep Neural Networks versus Glassy Systems.
ICML
(2018)
Stefano Sarao Mannelli
,
Giulio Biroli
,
Chiara Cammarota
,
Florent Krzakala
,
Pierfrancesco Urbani
,
Lenka Zdeborová
Marvels and Pitfalls of the Langevin Algorithm in Noisy High-dimensional Inference.
CoRR
(2018)
Vanni Bucci
,
Serena Bradde
,
Giulio Biroli
,
João B. Xavier
Social Interaction, Noise and Antibiotic-Mediated Switches in the Intestinal Microbiota.
PLoS Comput. Biol.
8 (4) (2012)
Giulio Biroli
,
Rémi Monasson
,
Martin Weigt
A variational description of the ground state structure in random satisfiability problems
CoRR
(1999)