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Physics-AI Fellow

Publications

Computational complexity of deep learning: fundamental limitations and empirical phenomena
B Barak, A Carrell, A Favero, W Li, L Stephan, A Zlokapa
– Journal of Statistical Mechanics: Theory and Experiment
(2024)
2024,
104008
LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging
K Wang, N Dimitriadis, A Favero, G Ortiz-Jimenez, F Fleuret, P Frossard
(2024)
What can be learnt with wide convolutional neural networks?*
F Cagnetta, A Favero, M Wyart
– Journal of Statistical Mechanics: Theory and Experiment
(2024)
2024,
104020
Probing the Latent Hierarchical Structure of Data via Diffusion Models
A Sclocchi, A Favero, NI Levi, M Wyart
(2024)
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
F Cagnetta, L Petrini, UM Tomasini, A Favero, M Wyart
– Physical Review X
(2024)
14,
031001
Multi-Modal Hallucination Control by Visual Information Grounding
A Favero, L Zancato, M Trager, S Choudhary, P Perera, A Achille, A Swaminathan, S Soatto
– 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
(2024)
00,
14303
Multi-Modal Hallucination Control by Visual Information Grounding
A Favero, L Zancato, M Trager, S Choudhary, P Perera, A Achille, A Swaminathan, S Soatto
(2024)
A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data
A Sclocchi, A Favero, M Wyart
(2024)
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
G Ortiz-Jimenez, A Favero, P Frossard
– Advances in Neural Information Processing Systems
(2023)
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
F Cagnetta, L Petrini, UM Tomasini, A Favero, M Wyart
(2023)
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Research Group

Relativity and Gravitation

Room

B0.30

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