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Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple Deep Neural Networks (DNNs) has shown state-of-the-art performances due to its variance reduction effect over Gaussian noises.
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Certifying confidence via randomized smoothing
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An image is worth 16x16 words: Transformers for image recognition at scale
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SmoothMix: Training confidence-calibrated smoothed classifiers for certified robustness
Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Do-Guk Kim, and Jinwoo Shin · 2021
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Miklós Z Horváth, Mark Niklas Mueller, Marc Fischer, and Martin Vechev · 2021
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Provably robust deep learning via adversarially trained smoothed classifiers
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Deep ensembles: A loss landscape perspective
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Overfitting in adversarially robust deep learning
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Randomized smoothing of all shapes and sizes
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Learning neural network subspaces
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Swin transformer: Hierarchical vision transformer using shifted windows
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Boosting the certified robustness of l-infinity distance nets
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Bayesian dividemix++ for enhanced learning with noisy labels
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