Publications

Review

2025

1. arXiv

   A Systematic Survey on Debugging Techniques for Machine Learning Systems

   Thanh-Dat Nguyen, Haoye Tian, Bach Le, and 2 more authors

   arXiv preprint arXiv:2503.03158 Mar 2025
2. TMLR

   VirDA: Reusing Backbone for Unsupervised Domain Adaptation with Visual Reprogramming

   Duc-Duy Nguyen, and Dat Nguyen

   Transactions on Machine Learning Research Mar 2025

2024

1. arXiv

   Inferring Properties of Graph Neural Networks

   Dat Nguyen, Hieu M. Vu, Cong-Thanh Le, and 4 more authors

   arXiv preprint arXiv:2401.03790 Jan 2024
2. ISSTA

   VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction

   Thanh-Dat Nguyen, Tung Do-Viet, Hung Nguyen-Duy, and 4 more authors

   arXiv preprint arXiv:2407.06826 Jul 2024
3. arXiv

   Combining Induction and Transduction for Abstract Reasoning

   Wen-Ding Li, Keya Hu, Carter Larsen, and 11 more authors

   arXiv preprint arXiv:2411.02272 Nov 2024

2023

1. arXiv

   Adversarial Attacks on Code Models with Discriminative Graph Patterns

   Thanh-Dat Nguyen, Yang Zhou, Xuan Bach D. Le, and 2 more authors

   arXiv preprint arXiv:2308.11161 Aug 2023

2022

1. ICSME

   FFL: Fine grained Fault Localization for Student Programs via Syntactic and Semantic Reasoning

   Thanh-Dat Nguyen, Thanh Le-Cong, Duc-Minh Luong, and 4 more authors

   In 2022 IEEE 38th International Conference on Software Maintenance and Evolution, Research Track Aug 2022
2. ICSE

   Toward the Analysis of Graph Neural Networks

   Thanh-Dat Nguyen, Thanh Le-Cong*, ThanhVu H. Nguyen, and 2 more authors

   In 2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results) Aug 2022

   Abs

   Graph Neural Networks (GNNs) have recently emerged as a robust framework for graph-structured data. They have been applied to many problems such as knowledge graph analysis, social networks recommendation, and even Covid19 detection and vaccine developments. However, unlike other deep neural networks such as Feed Forward Neural Networks (FFNNs), few analyses such as verification and property inferences exist, potentially due to dynamic behaviors of GNNs, which can take arbitrary graphs as input, whereas FFNNs which only take fixed size numerical vectors as inputs. This paper proposes an approach to analyze GNNs by converting them into FFNNs and reusing existing FFNNs analyses. We discuss various designs to ensure the scalability and accuracy of the conversions. We illustrate our method on a study case of node classification. We believe that our approach opens new research directions for understanding and analyzing GNNs.

2021

2020

1. ICPR

   End-to-End Hierarchical Relation Extraction for Generic Form Understanding

   Tuan-Anh Nguyen Dang, Duc Thanh Hoang, Quang Bach Tran, and 2 more authors

   ICPR 2020 Aug 2020
2. MAPR

   PCA-based 3D Facial Reenactment From Single Image

   Thanh-Dat Nguyen, Tuan-Anh Nguyen Dang, and Viet Sang Dinh

   In Aug 2020

2019

1. BMVC

   End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional UNet

   Tuan Anh Nguyen Dang, and Thanh-Dat Nguyen

   British Machine Vision Conference (BMVC) Aug 2019
2. Non-local DenseNet for plant CLEF 2019 contest

   Thanh-Dat Nguyen, George Quénot, and Lorraine Goeuriot

   CEUR-Workshop Aug 2019