
Physics-AI Fellow
Publications
Computational complexity of deep learning: fundamental limitations and empirical phenomena
– Journal of Statistical Mechanics: Theory and Experiment
(2024)
2024,
104008
(doi: 10.1088/1742-5468/ad3a5b)
LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging
(2024)
What can be learnt with wide convolutional neural networks?*
– Journal of Statistical Mechanics: Theory and Experiment
(2024)
2024,
104020
(doi: 10.1088/1742-5468/ad65df)
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
– Physical Review X
(2024)
14,
031001
(doi: 10.1103/PhysRevX.14.031001)
Multi-Modal Hallucination Control by Visual Information Grounding
– 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
(2024)
00,
14303
(doi: 10.1109/cvpr52733.2024.01356)
A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data
(2024)
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
– Advances in Neural Information Processing Systems
(2023)
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
(2023)
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