212 citations · 799 across the 27 of their papers we have counts for
4 papers · 1 filter
Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data
Xuran Meng, Difan Zou, Yuan Cao
Modern deep learning models are usually highly over-parameterized so that they can overfit the training data. Surprisingly, such overfitting neural networks can usually still achie…
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks
Yuan Cao, Difan Zou, Yuanzhi Li +1
We study the implicit bias of batch normalization trained by gradient descent. We show that when learning a linear model with batch normalization for binary classification, gradien…
Per-Example Gradient Regularization Improves Learning Signals from Noisy Data
Xuran Meng, Yuan Cao, Difan Zou
Gradient regularization, as described in \citet{barrett2021implicit}, is a highly effective technique for promoting flat minima during gradient descent. Empirical evidence suggests…
The Benefits of Mixup for Feature Learning
Difan Zou, Yuan Cao, Yuanzhi Li +1
Mixup, a simple data augmentation method that randomly mixes two data points via linear interpolation, has been extensively applied in various deep learning applications to gain be…