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Giulio Biroli
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2020 – today
- 2024
- [c12]Simon Martin, Francis Bach, Giulio Biroli:
On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions. AISTATS 2024: 3655-3663 - [i25]Giulio Biroli, Tony Bonnaire, Valentin De Bortoli, Marc Mézard:
Dynamical Regimes of Diffusion Models. CoRR abs/2402.18491 (2024) - [i24]Tony Bonnaire, Giulio Biroli, Chiara Cammarota:
From Zero to Hero: How local curvature at artless initial conditions leads away from bad minima. CoRR abs/2403.02418 (2024) - [i23]Dimitrios Bachtis, Giulio Biroli, Aurélien Decelle, Beatriz Seoane:
Cascade of phase transitions in the training of Energy-based models. CoRR abs/2405.14689 (2024) - [i22]Giulio Biroli, Marc Mézard:
Kernel Density Estimators in Large Dimensions. CoRR abs/2408.05807 (2024) - 2022
- [c11]Franco Pellegrini, Giulio Biroli:
Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks. ICML 2022: 17601-17626 - [i21]Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli:
Optimal learning rate schedules in high-dimensional non-convex optimization problems. CoRR abs/2202.04509 (2022) - [i20]Tanguy Marchand, Misaki Ozawa, Giulio Biroli, Stéphane Mallat:
Wavelet Conditional Renormalization Group. CoRR abs/2207.04941 (2022) - 2021
- [c10]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: 2286-2296 - [c9]Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli:
On the interplay between data structure and loss function in classification problems. NeurIPS 2021: 8506-8517 - [i19]Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli:
More data or more parameters? Investigating the effect of data structure on generalization. CoRR abs/2103.05524 (2021) - [i18]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 abs/2103.10697 (2021) - [i17]Franco Pellegrini, Giulio Biroli:
Sifting out the features by pruning: Are convolutional networks the winning lottery ticket of fully connected ones? CoRR abs/2104.13343 (2021) - [i16]Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Ari Morcos:
Transformed CNNs: recasting pre-trained convolutional layers with self-attention. CoRR abs/2106.05795 (2021) - 2020
- [j2]Felix Roy
, Matthieu Barbier
Complex interactions can create persistent fluctuations in high-diversity ecosystems. PLoS Comput. Biol. 16(5) (2020) - [c8]Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime. ICML 2020: 2280-2290 - [c7]Antoine Maillard, Gérard Ben Arous, Giulio Biroli:
Landscape Complexity for the Empirical Risk of Generalized Linear Models. MSML 2020: 287-327 - [c6]Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval. NeurIPS 2020 - [c5]Franco Pellegrini, Giulio Biroli:
An analytic theory of shallow networks dynamics for hinge loss classification. NeurIPS 2020 - [c4]Stéphane d'Ascoli, Levent Sagun, Giulio Biroli:
Triple descent and the two kinds of overfitting: where & why do they appear? NeurIPS 2020 - [i15]Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala:
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime. CoRR abs/2003.01054 (2020) - [i14]Stéphane d'Ascoli, Levent Sagun, Giulio Biroli:
Triple descent and the two kinds of overfitting: Where & why do they appear? CoRR abs/2006.03509 (2020) - [i13]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 abs/2006.06997 (2020) - [i12]Franco Pellegrini, Giulio Biroli:
An analytic theory of shallow networks dynamics for hinge loss classification. CoRR abs/2006.11209 (2020)
2010 – 2019
- 2019
- [c3]Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models. NeurIPS 2019: 8676-8686 - [c2]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: 9330-9340 - [i11]Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli
Scaling description of generalization with number of parameters in deep learning. CoRR abs/1901.01608 (2019) - [i10]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 abs/1905.12294 (2019) - [i9]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 abs/1906.01103 (2019) - [i8]Stéphane d'Ascoli
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. CoRR abs/1906.06766 (2019) - [i7]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 abs/1907.08226 (2019) - [i6]Antoine Maillard
Landscape Complexity for the Empirical Risk of Generalized Linear Models. CoRR abs/1912.02143 (2019) - 2018
- [c1]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: 324-333 - [i5]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 abs/1803.06969 (2018) - [i4]Mario Geiger, Stefano Spigler, Stéphane d'Ascoli
The jamming transition as a paradigm to understand the loss landscape of deep neural networks. CoRR abs/1809.09349 (2018) - [i3]Stefano Spigler, Mario Geiger, Stéphane d'Ascoli
A jamming transition from under- to over-parametrization affects loss landscape and generalization. CoRR abs/1810.09665 (2018) - [i2]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 abs/1812.09066 (2018) - 2012
- [j1]Vanni Bucci
Social Interaction, Noise and Antibiotic-Mediated Switches in the Intestinal Microbiota. PLoS Comput. Biol. 8(4) (2012)
2000 – 2009
- 2009
- [r1]Ludovic Berthier, Giulio Biroli:
Glasses and Aging, A Statistical Mechanics Perspective on. Encyclopedia of Complexity and Systems Science 2009: 4209-4240
1990 – 1999
- 1999
- [i1]Giulio Biroli, Rémi Monasson, Martin Weigt:
A variational description of the ground state structure in random satisfiability problems. CoRR cond-mat/9907343 (1999)
Coauthor Index
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last updated on 2024-09-18 23:42 CEST by the dblp team
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