no code implementations • 6 Apr 2023 • Yuke Hu, Wei Liang, Ruofan Wu, Kai Xiao, Weiqiang Wang, Xiaochen Li, Jinfei Liu, Zhan Qin
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks.
no code implementations • 31 Dec 2021 • Sung Min Park, Kuo-An Wei, Kai Xiao, Jerry Li, Aleksander Madry
We identify properties of universal adversarial perturbations (UAPs) that distinguish them from standard adversarial perturbations.
no code implementations • 3 Jul 2021 • Hui Li, Xing Fu, Ruofan Wu, Jinyu Xu, Kai Xiao, xiaofu Chang, Weiqiang Wang, Shuai Chen, Leilei Shi, Tao Xiong, Yuan Qi
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.
1 code implementation • 7 Jun 2021 • Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, Aleksander Madry
We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation.
1 code implementation • ICLR 2021 • Kai Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry
We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds.
no code implementations • ICLR 2020 • Krishnamurthy (Dj) Dvijotham, Jamie Hayes, Borja Balle, Zico Kolter, Chongli Qin, Andras Gyorgy, Kai Xiao, Sven Gowal, Pushmeet Kohli
Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results.
no code implementations • 14 Mar 2018 • Artem Maksov, Ondrej Dyck, Kai Wang, Kai Xiao, David B. Geohegan, Bobby G. Sumpter, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin, Maxim Ziatdinov
Understanding elementary mechanisms behind solid-state phase transformations and reactions is the key to optimizing desired functional properties of many technologically relevant materials.
6 code implementations • ICLR 2019 • Vincent Tjeng, Kai Xiao, Russ Tedrake
The computational speedup allows us to verify properties on convolutional networks with an order of magnitude more ReLUs than networks previously verified by any complete verifier.