122 citations · 239 across the 37 of their papers we have counts for
37 papers
Optimal Transport Model Distributional Robustness
Van-Anh Nguyen, Trung Le, Anh Tuan Bui +2
Distributional robustness is a promising framework for training deep learning models that are less vulnerable to adversarial examples and data distribution shifts. Previous works h…
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
Anh Bui, Trung Le, He Zhao +3
Deep learning models, even the-state-of-the-art ones, are highly vulnerable to adversarial examples. Adversarial training is one of the most efficient methods to improve the model'…
Flat Seeking Bayesian Neural Networks
Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan +3
Bayesian Neural Networks (BNNs) provide a probabilistic interpretation for deep learning models by imposing a prior distribution over model parameters and inferring a posterior dis…
Multiple Perturbation Attack: Attack Pixelwise Under Different -norms For Better Adversarial Performance
Ngoc N. Tran, Anh Tuan Bui, Dinh Phung +1
Adversarial machine learning has been both a major concern and a hot topic recently, especially with the ubiquitous use of deep neural networks in the current landscape. Adversaria…
An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning
Quyen Tran, Hai Nguyen, Hoang Phan +6
In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay valu…
Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective
Hoang Phan, Lam Tran, Quyen Tran +6
Multi-task learning (MTL) trains deep neural networks to optimize several objectives simultaneously using a shared backbone, which leads to reduced computational costs, improved da…