no code implementations • 6 Feb 2024 • Hao Wang, Xin Zhang, JinZhe Jiang, YaQian Zhao, Chen Li
However, it has been shown that multimodal NLP are vulnerable to adversarial attacks, where the outputs of a model can be dramatically changed by a perturbation to the input.
no code implementations • 22 Mar 2023 • Hao Wang, Chen Li, JinZhe Jiang, Xin Zhang, YaQian Zhao, Weifeng Gong
Recently, the robustness of deep learning models has received widespread attention, and various methods for improving model robustness have been proposed, including adversarial training, model architecture modification, design of loss functions, certified defenses, and so on.
no code implementations • 18 Jun 2021 • Chen Li, JinZhe Jiang, Xin Zhang, Tonghuan Zhang, YaQian Zhao, Dongdong Jiang, RenGang Li
Interpreting how does deep neural networks (DNNs) make predictions is a vital field in artificial intelligence, which hinders wide applications of DNNs.
no code implementations • 26 Feb 2021 • Chen Li, JinZhe Jiang, YaQian Zhao, RenGang Li, EnDong Wang, Xin Zhang, Kun Zhao
Decision of transfer layers and trainable layers is a major task for design of the transfer convolutional neural networks (CNN).
no code implementations • 2 Jun 2020 • JinZhe Jiang, Xin Zhang, Chen Li, YaQian Zhao, RenGang Li
In this model, we mapped the feature data to a quantum state in Hilbert space firstly, and then implement unitary evolution on it, in the end, we can get the classification result by im-plement measurement on the quantum state.