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cs.LG2024
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…
cs.LG2023
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…
cs.LG2021
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping
Xuran Meng, Jianfeng Yao
Much research effort has been devoted to explaining the success of deep learning. Random Matrix Theory (RMT) provides an emerging way to this end: spectral analysis of large random…