Search Results for author: Javid Ebrahimi

Found 10 papers, 2 papers with code

Embedding Compression with Hashing for Efficient Representation Learning in Graph

no code implementations29 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.

Natural Language Processing Representation Learning

Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks

no code implementations21 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.

Time Series Time Series Prediction

How Does Adversarial Fine-Tuning Benefit BERT?

no code implementations31 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.

Continual Learning Dependency Parsing +2

How Can Self-Attention Networks Recognize Dyck-n Languages?

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$\%$.

On Adversarial Examples for Character-Level Neural Machine Translation

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.

Machine Translation Translation

HotFlip: White-Box Adversarial Examples for Text Classification

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.

Classification General Classification +1

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