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The benefits of mixup for feature learning
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 …
interpolation, has been extensively applied in various deep learning applications to gain …
Initialization-dependent sample complexity of linear predictors and neural networks
R Magen, O Shamir - Advances in Neural Information …, 2023 - proceedings.neurips.cc
We provide several new results on the sample complexity of vector-valued linear predictors
(parameterized by a matrix), and more generally neural networks. Focusing on size …
(parameterized by a matrix), and more generally neural networks. Focusing on size …
Lower generalization bounds for gd and sgd in smooth stochastic convex optimization
This work studies the generalization error of gradient methods. More specifically, we focus
on how training steps $ T $ and step-size $\eta $ might affect generalization in smooth …
on how training steps $ T $ and step-size $\eta $ might affect generalization in smooth …
Implicit regularization of AdaDelta
We consider the AdaDelta adaptive optimization algorithm on locally Lipschitz, positively
homogeneous, and o-minimally definable neural networks, with either the exponential or the …
homogeneous, and o-minimally definable neural networks, with either the exponential or the …
[หนังสือ][B] Feature Learning in Neural Networks and Other Stochastic Explorations
M Glasgow - 2024 - search.proquest.com
Recent years have empirically demonstrated the unprecedented success of deep learning.
Yet our theoretical understanding of why gradient descent succeeds in training neural …
Yet our theoretical understanding of why gradient descent succeeds in training neural …