activity
20182022
most citedA Unified Wasserstein Distributional Robustness Framework for Adversarial Training

6 citations · 26 across the 11 of their papers we have counts for

collaborators

15 papers

cs.CV20223 cited

Vision Transformer Visualization: What Neurons Tell and How Neurons Behave?

Van-Anh Nguyen, Khanh Pham Dinh, Long Tung Vuong +4

Recently vision transformers (ViT) have been applied successfully for various tasks in computer vision. However, important questions such as why they work or how they behave still…

cs.LG20226 cited

A Unified Wasserstein Distributional Robustness Framework for Adversarial Training

Tuan Anh Bui, Trung Le, Quan Tran +2

It is well-known that deep neural networks (DNNs) are susceptible to adversarial attacks, exposing a severe fragility of deep learning systems. As the result, adversarial training…

cs.CL2021

Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning

Jianguo Zhang, Trung Bui, Seunghyun Yoon +6

In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-…

cs.CL20213 cited

Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference

Tuan Lai, Heng Ji, ChengXiang Zhai +1

Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any e…

cs.CL20214 cited

A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution

Tuan Lai, Heng Ji, Trung Bui +3

Event coreference resolution is an important research problem with many applications. Despite the recent remarkable success of pretrained language models, we argue that it is still…

cs.CL20201 cited

Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering

Quan Tran, Nhan Dam, Tuan Lai +4

Interpretability and explainability of deep neural networks are challenging due to their scale, complexity, and the agreeable notions on which the explaining process rests. Previou…