4 papers · 1 filter
How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?
Wei Huang, Andi Han, Yujin Song +4
The capacity of deep learning models is often large enough to both learn the underlying statistical signal and overfit to noise in the training set. This noise memorization can be…
Understanding the Generalization of Stochastic Gradient Adam in Learning Neural Networks
Xuan Tang, Han Zhang, Yuan Cao +1
Adam is a popular and widely used adaptive gradient method in deep learning, which has also received tremendous focus in theoretical research. However, most existing theoretical wo…
Initialization Matters: On the Benign Overfitting of Two-Layer ReLU CNN with Fully Trainable Layers
Shuning Shang, Xuran Meng, Yuan Cao +1
Benign overfitting refers to how over-parameterized neural networks can fit training data perfectly and generalize well to unseen data. While this has been widely investigated theo…
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…