Kaidi Xu
Kaidi Xu
Assistant Professor, Drexel University
Verified email at - Homepage
Cited by
Cited by
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
K Xu, H Chen, S Liu, PY Chen, TW Weng, M Hong, X Lin
(IJCAI-2019) The International Joint Conferences on Artificial Intelligence, 2019
Adversarial T-shirt! Evading Person Detectors in a Physical World
K Xu, G Zhang, S Liu, Q Fan, M Sun, H Chen, PY Chen, Y Wang, X Lin
(ECCV-2020 Spotlight) The European Conference on Computer Vision, 665-681, 2020
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
K Xu, S Liu, P Zhao, PY Chen, H Zhang, D Erdogmus, Y Wang, X Lin
(ICLR-2019) The International Conference on Learning Representations, 2018
Adversarial Robustness vs. Model Compression, or Both?
S Ye, K Xu, S Liu, H Cheng, JH Lambrechts, H Zhang, A Zhou, K Ma, ...
(ICCV-2019) The International Conference on Computer Vision, 2019
Progressive DNN Compression: A Key to Achieve Ultra-high Weight Pruning and Quantization Rates Using ADMM
S Ye, X Feng, T Zhang, X Ma, S Lin, Z Li, K Xu, W Wen, S Liu, J Tang, ...
arXiv preprint arXiv:1903.09769, 2019
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
K Xu, Z Shi, H Zhang, Y Wang, KW Chang, M Huang, B Kailkhura, X Lin, ...
(NeurIPS-2020) Advances in Neural Information Processing Systems, 2020
REQ-YOLO: A Resource-aware, Efficient Quantization Framework for Object Detection on FPGAs
C Ding, S Wang, N Liu, K Xu, Y Wang, Y Liang
(FPGA-2019) Proceedings of the 2019 ACM/SIGDA International Symposium on …, 2019
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
X Chen, S Liu, K Xu, X Li, X Lin, M Hong, D Cox
(NeurIPS-2019) Advances in Neural Information Processing Systems, 2019
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
P Zhao, S Liu, PY Chen, N Hoang, K Xu, B Kailkhura, X Lin
(ICCV-2019) The International Conference on Computer Vision, 2019
Min-max Optimization without Gradients: Convergence and Applications to Black-box Evasion and Poisoning Attacks
S Liu, S Lu, X Chen, Y Feng, K Xu, A Al-Dujaili, M Hong, UM O’Reilly
(ICML-2020) The International Conference on Machine Learning, 2020
Asymmetric Discrete Graph Hashing
X Shi, F Xing, K Xu, M Sapkota, L Yang
(AAAI-2017) The Association for the Advancement of Artificial Intelligence, 2017
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification
S Wang, H Zhang, K Xu, X Lin, S Jana, CJ Hsieh, JZ Kolter
(NeurIPS-2021) Advances in Neural Information Processing Systems, 2021
Interpreting Adversarial Examples by Activation Promotion and Suppression
K Xu, S Liu, G Zhang, M Sun, P Zhao, Q Fan, C Gan, X Lin
arXiv preprint arXiv:1904.02057, 2019
Fast and complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers
K Xu, H Zhang, S Wang, Y Wang, S Jana, X Lin, CJ Hsieh
(ICLR-2021) The International Conference on Learning Representations, 2020
Supervised Graph Hashing for Histopathology Image Retrieval and Classification
X Shi, F Xing, K Xu, Y Xie, H Su, L Yang
Medical image analysis 42, 117-128, 2017
Defending against Backdoor Attack on Deep Neural Networks
K Xu, S Liu, PY Chen, P Zhao, X Lin
(KDD workshop-2019) 3rd Workshop on Adversarial Learning Methods for Machine …, 2020
ADMM Attack: an Enhanced Adversarial Attack for Deep Neural Networks with Undetectable Distortions
P Zhao, K Xu, S Liu, Y Wang, X Lin
(ASPDAC-2019) Proceedings of the 24th Asia and South Pacific Design …, 2019
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
S Ye, K Wu, M Zhou, Y Yang, K Xu, J Song, C Bao, K Ma
(CVPR-2020) The Conference on Computer Vision and Pattern Recognition, 2019
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
R Wang, K Xu, S Liu, PY Chen, TW Weng, C Gan, M Wang
(ICLR-2021) The International Conference on Learning Representations, 2021
Loss-based attention for interpreting image-level prediction of convolutional neural networks
X Shi, F Xing, K Xu, P Chen, Y Liang, Z Lu, Z Guo
IEEE Transactions on Image Processing 30, 1662-1675, 2020
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