b'Trung Le'

research

∙ 06/07/2023

Optimal Transport Model Distributional Robustness

Distributional robustness is a promising framework for training deep lea...

0 Van-Anh Nguyen, et al. ∙

research

∙ 05/26/2023

AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities

Many ML-based approaches have been proposed to automatically detect, loc...

0 Michael Fu, et al. ∙

research

∙ 05/26/2023

Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities

Deep learning (DL) models have become increasingly popular in identifyin...

0 Michael Fu, et al. ∙

research

∙ 05/25/2023

Learning Directed Graphical Models with Optimal Transport

Estimating the parameters of a probabilistic directed graphical model fr...

0 Vy Vo, et al. ∙

research

∙ 05/17/2023

Sharpness Shift-Aware Self-Supervised Learning

Self-supervised learning aims to extract meaningful features from unlabe...

0 Ngoc N. Tran, et al. ∙

research

∙ 04/26/2023

Generating Adversarial Examples with Task Oriented Multi-Objective Optimization

Deep learning models, even the-state-of-the-art ones, are highly vulnera...

0 Anh Bui, et al. ∙

research

∙ 04/21/2023

Hyperbolic Geometry in Computer Vision: A Survey

Hyperbolic geometry, a Riemannian manifold endowed with constant section...

0 Pengfei Fang, et al. ∙

research

∙ 02/12/2023

Vector Quantized Wasserstein Auto-Encoder

Learning deep discrete latent presentations offers a promise of better s...

0 Tung-Long Vuong, et al. ∙

research

∙ 02/06/2023

Flat Seeking Bayesian Neural Networks

Bayesian Neural Networks (BNNs) offer a probabilistic interpretation for...

0 Van-Anh Nguyen, et al. ∙

research

∙ 12/05/2022

Multiple Perturbation Attack: Attack Pixelwise Under Different ℓ_p-norms For Better Adversarial Performance

Adversarial machine learning has been both a major concern and a hot top...

0 Ngoc N. Tran, et al. ∙

research

∙ 11/30/2022

Continual Learning with Optimal Transport based Mixture Model

Online Class Incremental learning (CIL) is a challenging setting in Cont...

0 Quyen Tran, et al. ∙

research

∙ 11/24/2022

Improving Multi-task Learning via Seeking Task-based Flat Regions

Multi-Task Learning (MTL) is a widely-used and powerful learning paradig...

0 Hoang Phan, et al. ∙

research

∙ 10/14/2022

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

Recently vision transformers (ViT) have been applied successfully for va...

0 Van-Anh Nguyen, et al. ∙

research

∙ 09/27/2022

Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations

Interpretable machine learning seeks to understand the reasoning process...

0 Vy Vo, et al. ∙

research

∙ 09/20/2022

An Information-Theoretic and Contrastive Learning-based Approach for Identifying Code Statements Causing Software Vulnerability

Software vulnerabilities existing in a program or function of computer s...

0 Van Nguyen, et al. ∙

research

∙ 09/19/2022

Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle

Software vulnerabilities (SVs) have become a common, serious and crucial...

0 Van Nguyen, et al. ∙

research

∙ 07/07/2022

An Additive Instance-Wise Approach to Multi-class Model Interpretation

Interpretable machine learning offers insights into what factors drive a...

0 Vy Vo, et al. ∙

research

∙ 06/09/2022

STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer

Modeling neural population dynamics underlying noisy single-trial spikin...

0 Trung Le, et al. ∙

research

∙ 06/04/2022

Stochastic Multiple Target Sampling Gradient Descent

Sampling from an unnormalized target distribution is an essential proble...

9 Hoang Phan, et al. ∙

research

∙ 03/01/2022

Global-Local Regularization Via Distributional Robustness

Despite superior performance in many situations, deep neural networks ar...

0 Hoang Phan, et al. ∙

research

∙ 02/27/2022

A Unified Wasserstein Distributional Robustness Framework for Adversarial Training

It is well-known that deep neural networks (DNNs) are susceptible to adv...

0 Tuan-Anh Bui, et al. ∙

research

∙ 11/27/2021

On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources

Domain adaptation (DA) benefits from the rigorous theoretical works that...

0 Trung Phung, et al. ∙

research

∙ 10/21/2021

Supporting Massive DLRM Inference Through Software Defined Memory

Deep Learning Recommendation Models (DLRM) are widespread, account for a...

0 Ehsan K. Ardestani, et al. ∙

research

∙ 10/14/2021

ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection

Identifying vulnerabilities in the source code is essential to protect t...

0 Van-Anh Nguyen, et al. ∙

research

∙ 05/13/2021

SyntheticFur dataset for neural rendering

We introduce a new dataset called SyntheticFur built specifically for ma...

20 Trung Le, et al. ∙

research

∙ 04/27/2021

Text Generation with Deep Variational GAN

Generating realistic sequences is a central task in many machine learnin...

0 Mahmoud Hossam, et al. ∙

research

∙ 04/27/2021

Improved and Efficient Text Adversarial Attacks using Target Information

There has been recently a growing interest in studying adversarial examp...

0 Mahmoud Hossam, et al. ∙

research

∙ 02/11/2021

BoMb-OT: On Batch of Mini-batches Optimal Transport

Mini-batch optimal transport (m-OT) has been successfully used in practi...

12 Khai Nguyen, et al. ∙

research

∙ 01/25/2021

Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning

Contrastive learning (CL) has recently emerged as an effective approach ...

0 Anh Bui, et al. ∙

research

∙ 12/04/2020

Offset Curves Loss for Imbalanced Problem in Medical Segmentation

Medical image segmentation has played an important role in medical analy...

20 Ngan Le, et al. ∙

research

∙ 11/05/2020

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

Interpretability and explainability of deep neural networks are challeng...

0 Quan Tran, et al. ∙

research

∙ 10/14/2020

Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability

Training robust deep learning models for down-stream tasks is a critical...

0 Mahmoud Hossam, et al. ∙

research

∙ 10/13/2020

Towards Understanding Pixel Vulnerability under Adversarial Attacks for Images

Deep neural network image classifiers are reported to be susceptible to ...

1 He Zhao, et al. ∙

research

∙ 09/21/2020

Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness

Ensemble-based adversarial training is a principled approach to achieve ...

8 Anh Bui, et al. ∙

research

∙ 08/12/2020

Neural Sinkhorn Topic Model

In this paper, we present a new topic modelling approach via the theory ...

0 He Zhao, et al. ∙

research

∙ 07/10/2020

Improving Adversarial Robustness by Enforcing Local and Global Compactness

The fact that deep neural networks are susceptible to crafted perturbati...

7 Anh Bui, et al. ∙

research

∙ 04/16/2020

OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

One of the challenging problems in sequence generation tasks is the opti...

22 Mahmoud Hossam, et al. ∙

research

∙ 10/03/2019

Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions

Deep neural network image classifiers are reported to be susceptible to ...

22 He Zhao, et al. ∙

research

∙ 01/25/2019

When Can Neural Networks Learn Connected Decision Regions?

Previous work has questioned the conditions under which the decision reg...

0 Trung Le, et al. ∙

research

∙ 11/15/2018

Theoretical Perspective of Deep Domain Adaptation

Deep domain adaptation has recently undergone a big success. Compared wi...

0 Trung Le, et al. ∙

research

∙ 11/06/2017

KGAN: How to Break The Minimax Game in GAN

Generative Adversarial Networks (GANs) were intuitively and attractively...

0 Trung Le, et al. ∙

research

∙ 09/19/2017

Analogical-based Bayesian Optimization

Some real-world problems revolve to solve the optimization problem _x∈Xf...

0 Trung Le, et al. ∙

research

∙ 09/12/2017

Dual Discriminator Generative Adversarial Nets

We propose in this paper a novel approach to tackle the problem of mode ...

0 Tu Dinh Nguyen, et al. ∙

research

∙ 08/16/2017

Geometric Enclosing Networks

Training model to generate data has increasingly attracted research atte...

0 Trung Le, et al. ∙

research

∙ 08/08/2017

Multi-Generator Generative Adversarial Nets

We propose a new approach to train the Generative Adversarial Nets (GANs...

0 Quan Hoang, et al. ∙

research

∙ 04/22/2016

Approximation Vector Machines for Large-scale Online Learning

One of the most challenging problems in kernel online learning is to bou...

0 Trung Le, et al. ∙

Trung Le

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