no code implementations • COLING 2016 • Javid Ebrahimi, Dejing Dou, Daniel Lowd
Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining.
2 code implementations • ACL 2018 • Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou
We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier.
3 code implementations • COLING 2018 • Javid Ebrahimi, Daniel Lowd, Dejing Dou
Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Javid Ebrahimi, Dhruv Gelda, Wei zhang
For $\mathcal{D}_2$, we find that SA$^-$ completely breaks down on long sequences whereas the accuracy of SA$^+$ is 58. 82$\%$.
no code implementations • 31 Aug 2021 • Javid Ebrahimi, Hao Yang, Wei zhang
Adversarial training (AT) is one of the most reliable methods for defending against adversarial attacks in machine learning.
no code implementations • 21 Sep 2021 • Chin-Chia Michael Yeh, Zhongfang Zhuang, Junpeng Wang, Yan Zheng, Javid Ebrahimi, Ryan Mercer, Liang Wang, Wei zhang
In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database.
no code implementations • 29 Sep 2021 • Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei zhang
When applying such type of networks on graph without node feature, one can extract simple graph-based node features (e. g., number of degrees) or learn the input node representation (i. e., embeddings) when training the network.
no code implementations • 11 Aug 2022 • Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei zhang
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer.
no code implementations • 11 Nov 2022 • Amir Pouran Ben Veyseh, Javid Ebrahimi, Franck Dernoncourt, Thien Huu Nguyen
Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i. e., participants) from text.