122 citations · 239 across the 37 of their papers we have counts for
7 papers · 1 filter
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…
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability
Mahmoud Hossam, Trung Le, He Zhao +1
Training robust deep learning models for down-stream tasks is a critical challenge. Research has shown that down-stream models can be easily fooled with adversarial inputs that loo…
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks
He Zhao, Thanh Nguyen, Trung Le +4
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Many advers…
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
Anh Bui, Trung Le, He Zhao +4
Ensemble-based adversarial training is a principled approach to achieve robustness against adversarial attacks. An important technique of this approach is to control the transferab…
Neural Topic Model via Optimal Transport
He Zhao, Dinh Phung, Viet Huynh +2
Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it…
Improving Adversarial Robustness by Enforcing Local and Global Compactness
Anh Bui, Trung Le, He Zhao +4
The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application. Among many developed defens…