An sde for modeling sam: Theory and insights
We study the SAM (Sharpness-Aware Minimization) optimizer which has recently attracted a
lot of interest due to its increased performance over more classical variants of stochastic …
lot of interest due to its increased performance over more classical variants of stochastic …
Decentralized SGD and average-direction SAM are asymptotically equivalent
Decentralized stochastic gradient descent (D-SGD) allows collaborative learning on
massive devices simultaneously without the control of a central server. However, existing …
massive devices simultaneously without the control of a central server. However, existing …
Fantastic robustness measures: the secrets of robust generalization
Adversarial training has become the de-facto standard method for improving the robustness
of models against adversarial examples. However, robust overfitting remains a significant …
of models against adversarial examples. However, robust overfitting remains a significant …
The crucial role of normalization in sharpness-aware minimization
Abstract Sharpness-Aware Minimization (SAM) is a recently proposed gradient-based
optimizer (Foret et al., ICLR 2021) that greatly improves the prediction performance of deep …
optimizer (Foret et al., ICLR 2021) that greatly improves the prediction performance of deep …
Fast sharpness-aware training for periodic time series classification and forecasting
Various deep learning architectures have been developed to capture long-term
dependencies in time series data, but challenges such as overfitting and computational time …
dependencies in time series data, but challenges such as overfitting and computational time …
Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy
Recently, sharpness-aware minimization (SAM) has attracted a lot of attention because of its
surprising effectiveness in improving generalization performance. However, training neural …
surprising effectiveness in improving generalization performance. However, training neural …
Improving Sharpness-Aware Minimization by Lookahead
Sharpness-Aware Minimization (SAM), which performs gradient descent on adversarially
perturbed weights, can improve generalization by identifying flatter minima. However, recent …
perturbed weights, can improve generalization by identifying flatter minima. However, recent …
Lookahead Sharpness-Aware Minimization
R Yu, Y Zhang, J Kwok - openreview.net
Sharpness-Aware Minimization (SAM), which performs gradient descent on adversarially
perturbed weights, can improve generalization by identifying flatter minima. However, recent …
perturbed weights, can improve generalization by identifying flatter minima. However, recent …