278 citations · 846 across the 26 of their papers we have counts for
6 papers · 1 filter
Scaling Compute Is Not All You Need for Adversarial Robustness
Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko +3
The last six years have witnessed significant progress in adversarially robust deep learning. As evidenced by the CIFAR-10 dataset category in RobustBench benchmark, the accuracy u…
Critical Influence of Overparameterization on Sharpness-aware Minimization
Sungbin Shin, Dongyeop Lee, Maksym Andriushchenko +1
Sharpness-Aware Minimization (SAM) has attracted considerable attention for its effectiveness in improving generalization in deep neural network training by explicitly minimizing s…
Layer-wise Linear Mode Connectivity
Linara Adilova, Maksym Andriushchenko, Michael Kamp +2
Averaging neural network parameters is an intuitive method for fusing the knowledge of two independent models. It is most prominently used in federated learning. If models are aver…
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
Klim Kireev, Maksym Andriushchenko, Carmela Troncoso +1
Research on adversarial robustness is primarily focused on image and text data. Yet, many scenarios in which lack of robustness can result in serious risks, such as fraud detection…
Sharpness-Aware Minimization Leads to Low-Rank Features
Maksym Andriushchenko, Dara Bahri, Hossein Mobahi +1
Sharpness-aware minimization (SAM) is a recently proposed method that minimizes the sharpness of the training loss of a neural network. While its generalization improvement is well…
A Modern Look at the Relationship between Sharpness and Generalization
Maksym Andriushchenko, Francesco Croce, Maximilian Müller +2
Sharpness of minima is a promising quantity that can correlate with generalization in deep networks and, when optimized during training, can improve generalization. However, standa…