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DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing
Jiawei Zhang, Zhongzhu Chen, Huan Zhang +2
Diffusion models have been leveraged to perform adversarial purification and thus provide both empirical and certified robustness for a standard model. On the other hand, different…
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Yihua Zhang, Ruisi Cai, Tianlong Chen +6
Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has demonstrated a great promise to enable high-accuracy and ultra-efficient model inference. Despite t…
Provably Bounding Neural Network Preimages
Suhas Kotha, Christopher Brix, Zico Kolter +2
Most work on the formal verification of neural networks has focused on bounding the set of outputs that correspond to a given set of inputs (for example, bounded perturbations of a…