Xiaomeng Jin
Cytowane przez
Cytowane przez
AdverTorch v0. 1: An adversarial robustness toolbox based on pytorch
GW Ding, L Wang, X Jin
arXiv preprint arXiv:1902.07623, 2019
On the Sensitivity of Adversarial Robustness to Input Data Distributions.
GW Ding, KYC Lui, X Jin, L Wang, R Huang
ICLR (poster) 4, 2019
Chemical-Reaction-Aware Molecule Representation Learning
H Wang, W Li, X Jin, K Cho, H Ji, J Han, MD Burke
International Conference on Learning Representations, 2021
Gender, confidence, and mark prediction in CS examinations
B Harrington, S Peng, X Jin, M Khan
Proceedings of the 23rd Annual ACM Conference on Innovation and Technology …, 2018
RESIN-11: Schema-guided event prediction for 11 newsworthy scenarios
X Du, Z Zhang, S Li, P Yu, H Wang, T Lai, X Lin, Z Wang, I Liu, B Zhou, ...
Proceedings of the 2022 Conference of the North American Chapter of the …, 2022
Event Schema Induction with Double Graph Autoencoders
X Jin, M Li, H Ji
2022 Annual Conference of the North American Chapter of the Association for …, 2022
Adversarial Robustness for Large Language NER models using Disentanglement and Word Attributions
X Jin, B Vinzamuri, S Venkatapathy, H Ji, P Natarajan
The 2023 Conference on Empirical Methods in Natural Language Processing, 2023
Schema-based Data Augmentation for Event Extraction
X Jin, H Ji
Proc. The 2024 Joint International Conference on Computational Linguistics …, 2024
Toward Consistent and Informative Event-Event Temporal Relation Extraction
X Jin, H Wen, X Du, H Ji
Proc. ACL2023 Workshop on Matching From Unstructured and Structured Data, 2023
System and method for machine learning architecture with adversarial attack defense
W Ding, L Wang, R Huang, JIN Xiaomeng, KYC Lui
US Patent 11,520,899, 2022
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