14 citations · 125 across the 88 of their papers we have counts for
15 papers · 1 filter
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…
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Thuy-Trang Vu, Shahram Khadivi, Xuanli He +2
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT). This paper investigates whether the domain information can be transf…
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…
Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle
Van Nguyen, Trung Le, Chakkrit Tantithamthavorn +3
Software vulnerabilities (SVs) have become a common, serious and crucial concern due to the ubiquity of computer software. Many machine learning-based approaches have been proposed…
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
Chuanxia Zheng, Long Tung Vuong, Jianfei Cai +1
Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar patches with…
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Vy Vo, Trung Le, Van Nguyen +4
Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach i…
Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive Learning
Van Nguyen, Trung Le, Chakkrit Tantithamthavorn +6
Software vulnerabilities are a serious and crucial concern. Typically, in a program or function consisting of hundreds or thousands of source code statements, there are only a few…
An Additive Instance-Wise Approach to Multi-class Model Interpretation
Vy Vo, Van Nguyen, Trung Le +4
Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying expla…
Stochastic Multiple Target Sampling Gradient Descent
Hoang Phan, Ngoc Tran, Trung Le +3
Sampling from an unnormalized target distribution is an essential problem with many applications in probabilistic inference. Stein Variational Gradient Descent (SVGD) has been show…
High-Quality Pluralistic Image Completion via Code Shared VQGAN
Chuanxia Zheng, Guoxian Song, Tat-Jen Cham +3
PICNet pioneered the generation of multiple and diverse results for image completion task, but it required a careful balance between loss (diversity) and reconstruct…
Global-Local Regularization Via Distributional Robustness
Hoang Phan, Trung Le, Trung Phung +3
Despite superior performance in many situations, deep neural networks are often vulnerable to adversarial examples and distribution shifts, limiting model generalization ability in…
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…
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
Tam Le, Truyen Nguyen, Dinh Phung +1
Optimal transport (OT) is a popular measure to compare probability distributions. However, OT suffers a few drawbacks such as (i) a high complexity for computation, (ii) indefinite…