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stat.ML2025
Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks
Chenyang Zhang, Peifeng Gao, Difan Zou +1
Modern neural networks are usually highly over-parameterized. Behind the wide usage of over-parameterized networks is the belief that, if the data are simple, then the trained netw…
stat.ML2025
On the Feature Learning in Diffusion Models
Andi Han, Wei Huang, Yuan Cao +1
The predominant success of diffusion models in generative modeling has spurred significant interest in understanding their theoretical foundations. In this work, we propose a featu…
stat.ML2024
The Implicit Bias of Adam on Separable Data
Chenyang Zhang, Difan Zou, Yuan Cao
Adam has become one of the most favored optimizers in deep learning problems. Despite its success in practice, numerous mysteries persist regarding its theoretical understanding. I…