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2020 – today
- 2024
[j20]Michael Fu, Chakkrit Tantithamthavorn
, Trung Le, Yuki Kume, Van Nguyen, Dinh Q. Phung, John C. Grundy:
AIBugHunter: A Practical tool for predicting, classifying and repairing software vulnerabilities. Empir. Softw. Eng. 29(1): 4 (2024)- [j19]Michael Fu, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Phung, Trung Le:
Vision Transformer Inspired Automated Vulnerability Repair. ACM Trans. Softw. Eng. Methodol. 33(3): 78:1-78:29 (2024) - [j18]Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John C. Grundy, Dinh Q. Phung:
Deep Domain Adaptation With Max-Margin Principle for Cross-Project Imbalanced Software Vulnerability Detection. ACM Trans. Softw. Eng. Methodol. 33(6): 162 (2024) - [c104]Minh-Tuan Tran, Trung Le, Xuan-May Thi Le, Mehrtash Harandi, Quan Hung Tran, Dinh Q. Phung:
NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation. CVPR 2024: 23860-23869 - [c103]Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Dinh Phung:
Text-Enhanced Data-Free Approach for Federated Class-Incremental Learning. CVPR 2024: 23870-23880 - [c102]Hoang Anh Dung, Cuong Pham, Trung Le, Jianfei Cai, Thanh-Toan Do:
Sharpness-Aware Data Generation for Zero-shot Quantization. ICML 2024 - [c101]Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung:
Optimal Transport for Structure Learning Under Missing Data. ICML 2024 - [c100]Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Phung:
Parameter Estimation in DAGs from Incomplete Data via Optimal Transport. ICML 2024 - [c99]Cuong Pham, Van-Anh Nguyen, Trung Le, Dinh Q. Phung, Gustavo Carneiro, Thanh-Toan Do:
Frequency Attention for Knowledge Distillation. WACV 2024: 2266-2275 - [i71]Parul Gupta, Tuan Nguyen, Abhinav Dhall, Munawar Hayat, Trung Le, Thanh-Toan Do:
DiffAugment: Diffusion based Long-Tailed Visual Relationship Recognition. CoRR abs/2401.01387 (2024) - [i70]Jing Wu, Trung Le, Munawar Hayat, Mehrtash Harandi:
EraseDiff: Erasing Data Influence in Diffusion Models. CoRR abs/2401.05779 (2024) - [i69]Tuan Nguyen, Van Nguyen, Trung Le, He Zhao, Quan Hung Tran, Dinh Q. Phung:
A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation. CoRR abs/2401.15952 (2024) - [i68]Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Q. Phung:
Optimal Transport for Structure Learning Under Missing Data. CoRR abs/2402.15255 (2024) - [i67]Cuong Pham, Van-Anh Nguyen, Trung Le, Dinh Q. Phung, Gustavo Carneiro, Thanh-Toan Do:
Frequency Attention for Knowledge Distillation. CoRR abs/2403.05894 (2024) - [i66]Anh Tuan Bui, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham, Dinh Q. Phung:
Removing Undesirable Concepts in Text-to-Image Generative Models with Learnable Prompts. CoRR abs/2403.12326 (2024) - [i65]Anh Bui, Vy Vo, Tung Pham, Dinh Q. Phung, Trung Le:
Diversity-Aware Agnostic Ensemble of Sharpness Minimizers. CoRR abs/2403.13204 (2024) - [i64]Minh-Tuan Tran, Trung Le, Xuan-May Thi Le, Mehrtash Harandi, Dinh Phung:
Text-Enhanced Data-free Approach for Federated Class-Incremental Learning. CoRR abs/2403.14101 (2024) - [i63]Van-Anh Nguyen, Quyen Tran, Tuan Truong, Thanh-Toan Do, Dinh Quoc Phung, Trung Le:
Agnostic Sharpness-Aware Minimization. CoRR abs/2406.07107 (2024) - [i62]Hoang Phan, Lam Tran, Quyen Tran, Trung Le:
Enhancing Domain Adaptation through Prompt Gradient Alignment. CoRR abs/2406.09353 (2024) - [i61]Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang, Yinwei Wei, Trung Le, Dragan Gasevic, Yuan-Fang Li, Thanh-Toan Do:
PromptDSI: Prompt-based Rehearsal-free Instance-wise Incremental Learning for Document Retrieval. CoRR abs/2406.12593 (2024) - [i60]Cuong Pham, Cuong C. Nguyen, Trung Le, Dinh Q. Phung, Gustavo Carneiro, Thanh-Toan Do:
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning. CoRR abs/2407.02721 (2024) - [i59]Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do:
MetaAug: Meta-Data Augmentation for Post-Training Quantization. CoRR abs/2407.14726 (2024) - [i58]Khanh Doan, Long Tung Vuong, Tuan Nguyen, Anh Tuan Bui, Quyen Tran, Thanh-Toan Do, Dinh Phung, Trung Le:
Connective Viewpoints of Signal-to-Noise Diffusion Models. CoRR abs/2408.04221 (2024) - 2023
- [j17]Peiyun Wu, Trung Le, Zhichun Zhu, Zhao Zhang:
Redundant Array of Independent Memory Devices. IEEE Comput. Archit. Lett. 22(2): 181-184 (2023) - [j16]Benzfica Hanif, Aisyah Larasati, Rudi Nurdiansyah, Trung Le:
The Effect of the Number of Hidden Layers on The Performance of Deep Q-Network for Traveling Salesman Problem. Knowl. Eng. Data Sci. 6(2): 188 (2023) - [j15]Alex Davila-Frias, Nita Yodo, Trung Le, Om Prakash Yadav:
A deep neural network and Bayesian method based framework for all-terminal network reliability estimation considering degradation. Reliab. Eng. Syst. Saf. 229: 108881 (2023) - [j14]Anh Tuan Bui, Trung Le, He Zhao, Quan Hung Tran, Paul Montague, Dinh Phung:
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization. Trans. Mach. Learn. Res. 2023 (2023) - [j13]Trung Le, Zhao Zhang, Zhichun Zhu:
Polling-Based Memory Interface. ACM Trans. Design Autom. Electr. Syst. 28(3): 47:1-47:23 (2023) - [j12]Michael Fu, Van Nguyen, Chakkrit Kla Tantithamthavorn, Trung Le, Dinh Q. Phung:
VulExplainer: A Transformer-Based Hierarchical Distillation for Explaining Vulnerability Types. IEEE Trans. Software Eng. 49(10): 4550-4565 (2023) - [c98]Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, Nhat Ho, Dinh Q. Phung:
Global-Local Regularization Via Distributional Robustness. AISTATS 2023: 7644-7664 - [c97]Michael Fu, Chakkrit Kla Tantithamthavorn, Van Nguyen, Trung Le:
ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We? APSEC 2023: 632-636 - [c96]Soumyadeep Hore, Quoc H. Nguyen, Yulun Xu, Ankit Shah, Nathaniel D. Bastian, Trung Le:
Empirical Evaluation of Autoencoder Models for Anomaly Detection in Packet-based NIDS. DSC 2023: 1-8 - [c95]Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran, Gholamreza Haffari, Seyit Camtepe, Dinh Phung:
An Additive Instance-Wise Approach to Multi-class Model Interpretation. ICLR 2023 - [c94]Long Tung Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Q. Phung:
Vector Quantized Wasserstein Auto-Encoder. ICML 2023: 35223-35242 - [c93]Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung:
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations. KDD 2023: 2211-2222 - [c92]Thanh Nguyen-Duc, Trung Le, Roland Bammer, He Zhao, Jianfei Cai, Dinh Q. Phung:
Cross-Adversarial Local Distribution Regularization for Semi-supervised Medical Image Segmentation. MICCAI (1) 2023: 183-194 - [c91]Jingyuan Li, Leo Scholl, Trung Le, Pavithra Rajeswaran, Amy Orsborn, Eli Shlizerman:
AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity. NeurIPS 2023 - [c90]Lu Mi, Trung Le, Tianxing He, Eli Shlizerman, Uygar Sümbül:
Learning Time-Invariant Representations for Individual Neurons from Population Dynamics. NeurIPS 2023 - [c89]Van-Anh Nguyen, Trung Le, Anh Tuan Bui, Thanh-Toan Do, Dinh Q. Phung:
Optimal Transport Model Distributional Robustness. NeurIPS 2023 - [c88]Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan, Thanh-Toan Do, Dinh Q. Phung, Trung Le:
Flat Seeking Bayesian Neural Networks. NeurIPS 2023 - [c87]Van Cuong Pham, Cuong C. Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do:
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning. NeurIPS 2023 - [c86]Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Phung:
Adversarial local distribution regularization for knowledge distillation. WACV 2023: 4670-4679 - [i57]Van-Anh Nguyen, Long Tung Vuong, Hoang Phan, Thanh-Toan Do, Dinh Q. Phung, Trung Le:
Flat Seeking Bayesian Neural Networks. CoRR abs/2302.02713 (2023) - [i56]Tung-Long Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Q. Phung:
Vector Quantized Wasserstein Auto-Encoder. CoRR abs/2302.05917 (2023) - [i55]Pengfei Fang, Mehrtash Harandi, Trung Le, Dinh Q. Phung:
Hyperbolic Geometry in Computer Vision: A Survey. CoRR abs/2304.10764 (2023) - [i54]Anh Tuan Bui, Trung Le, He Zhao, Quan Hung Tran, Paul Montague, Dinh Q. Phung:
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization. CoRR abs/2304.13229 (2023) - [i53]Ngoc N. Tran, Son Duong, Hoang Phan, Tung Pham, Dinh Q. Phung, Trung Le:
Sharpness & Shift-Aware Self-Supervised Learning. CoRR abs/2305.10252 (2023) - [i52]Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Q. Phung:
Learning Directed Graphical Models with Optimal Transport. CoRR abs/2305.15927 (2023) - [i51]Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Yuki Kume, Van Nguyen, Dinh Phung, John C. Grundy:
AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities. CoRR abs/2305.16615 (2023) - [i50]Van-Anh Nguyen, Trung Le, Anh Tuan Bui, Thanh-Toan Do, Dinh Q. Phung:
Optimal Transport Model Distributional Robustness. CoRR abs/2306.04178 (2023) - [i49]Michael Fu, Trung Le, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Q. Phung:
Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities. CoRR abs/2306.06109 (2023) - [i48]Tuan Truong, Hoang-Phi Nguyen, Tung Pham, Minh-Tuan Tran, Mehrtash Harandi, Dinh Phung, Trung Le:
RSAM: Learning on manifolds with Riemannian Sharpness-aware Minimization. CoRR abs/2309.17215 (2023) - [i47]Minh-Tuan Tran, Trung Le, Xuan-May Thi Le, Mehrtash Harandi, Quan Hung Tran, Dinh Q. Phung:
Unleash Data Generation for Efficient and Effective Data-free Knowledge Distillation. CoRR abs/2310.00258 (2023) - [i46]Thanh Nguyen-Duc, Trung Le, Roland Bammer, He Zhao, Jianfei Cai, Dinh Q. Phung:
Cross-adversarial local distribution regularization for semi-supervised medical image segmentation. CoRR abs/2310.01176 (2023) - [i45]Michael Fu, Chakkrit Tantithamthavorn, Van Nguyen, Trung Le:
ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We? CoRR abs/2310.09810 (2023) - [i44]Lu Mi, Trung Le, Tianxing He, Eli Shlizerman, Uygar Sümbül:
Learning Time-Invariant Representations for Individual Neurons from Population Dynamics. CoRR abs/2311.02258 (2023) - [i43]Ngoc N. Tran, Lam Tran, Hoang Phan, Anh Tuan Bui, Tung Pham, Toan Tran, Dinh Q. Phung, Trung Le:
Robust Contrastive Learning With Theory Guarantee. CoRR abs/2311.09671 (2023) - [i42]Quyen Tran, Lam Tran, Khoat Than, Toan Tran, Dinh Q. Phung, Trung Le:
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All. CoRR abs/2311.15414 (2023) - [i41]Khanh Doan, Quyen Tran, Tuan Nguyen, Dinh Q. Phung, Trung Le:
Class-Prototype Conditional Diffusion Model for Continual Learning with Generative Replay. CoRR abs/2312.06710 (2023) - 2022
- [j11]Thien Pham, Trung Le, Dat Dang, Hung Bui, Hoang Pham, Loi Truong, Mao Nguyen, Thanh Hung Vo, Quan Thanh Tho:
ARNS: A Data-Driven Approach for SoH Estimation of Lithium-Ion Battery Using Nested Sequence Models With Considering Relaxation Effect. IEEE Access 10: 117067-117083 (2022) - [j10]Trung Le, Khanh Nguyen, Dinh Q. Phung:
Improving kernel online learning with a snapshot memory. Mach. Learn. 111(3): 997-1018 (2022) - [j9]Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Geoffrey I. Webb, Dinh Phung:
Robust Variational Learning for Multiclass Kernel Models With Stein Refinement. IEEE Trans. Knowl. Data Eng. 34(9): 4425-4438 (2022) - [c85]Thanh Nguyen-Duc, Trung Le, He Zhao, Jianfei Cai, Dinh Q. Phung:
Particle-based Adversarial Local Distribution Regularization. AISTATS 2022: 5212-5224 - [c84]Trung Le, Anh Tuan Bui, Le Minh Tri Tue, He Zhao, Paul Montague, Quan Hung Tran, Dinh Q. Phung:
On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds. AISTATS 2022: 11438-11460 - [c83]Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Jens Axboe, Valmiki Rampersad, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Dheevatsa Mudigere, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Krishnakumar Nair, Maxim Naumov, Chris Petersen, Mikhail Smelyanskiy, Vijay Rao:
Supporting Massive DLRM Inference through Software Defined Memory. ICDCS 2022: 302-312 - [c82]Anh Tuan Bui, Trung Le, Quan Hung Tran, He Zhao, Dinh Q. Phung:
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. ICLR 2022 - [c81]Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho:
On Transportation of Mini-batches: A Hierarchical Approach. ICML 2022: 16622-16655 - [c80]Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, Dinh Phung:
ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection. ICSE-Companion 2022: 178-182 - [c79]Trung Le, Eli Shlizerman:
STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers. NeurIPS 2022 - [c78]Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung:
Stochastic Multiple Target Sampling Gradient Descent. NeurIPS 2022 - [c77]Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Van Nguyen, Dinh Q. Phung:
VulRepair: a T5-based automated software vulnerability repair. ESEC/SIGSOFT FSE 2022: 935-947 - [c76]Tuan Nguyen, Van Nguyen, Trung Le, He Zhao, Quan Hung Tran, Dinh Q. Phung:
Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation. UAI 2022: 1519-1529 - [i40]Tuan-Anh Bui, Trung Le, Quan Hung Tran, He Zhao, Dinh Q. Phung:
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. CoRR abs/2202.13437 (2022) - [i39]Hoang Phan, Trung Le, Trung Phung, Tuan-Anh Bui, Nhat Ho, Dinh Q. Phung:
Global-Local Regularization Via Distributional Robustness. CoRR abs/2203.00553 (2022) - [i38]Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung:
Stochastic Multiple Target Sampling Gradient Descent. CoRR abs/2206.01934 (2022) - [i37]Trung Le, Eli Shlizerman:
STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer. CoRR abs/2206.04727 (2022) - [i36]Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran, Gholamreza Haffari, Seyit Camtepe, Dinh Q. Phung:
An Additive Instance-Wise Approach to Multi-class Model Interpretation. CoRR abs/2207.03113 (2022) - [i35]Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John C. Grundy, Hung Nguyen, Dinh Q. Phung:
Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle. CoRR abs/2209.10406 (2022) - [i34]Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John C. Grundy, Hung Nguyen, Seyit Camtepe, Paul Quirk, Dinh Q. Phung:
An Information-Theoretic and Contrastive Learning-based Approach for Identifying Code Statements Causing Software Vulnerability. CoRR abs/2209.10414 (2022) - [i33]Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, Dinh Q. Phung:
Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations. CoRR abs/2209.13446 (2022) - [i32]Van-Anh Nguyen, Khanh Pham Dinh, Long Tung Vuong, Thanh-Toan Do, Quan Hung Tran, Dinh Q. Phung, Trung Le:
Vision Transformer Visualization: What Neurons Tell and How Neurons Behave? CoRR abs/2210.07646 (2022) - [i31]Hoang Phan, Lam Tran, Ngoc N. Tran, Nhat Ho, Dinh Q. Phung, Trung Le:
Improving Multi-task Learning via Seeking Task-based Flat Regions. CoRR abs/2211.13723 (2022) - [i30]Quyen Tran, Hoang Phan, Khoat Than, Dinh Q. Phung, Trung Le:
Continual Learning with Optimal Transport based Mixture Model. CoRR abs/2211.16780 (2022) - [i29]Ngoc N. Tran, Anh Tuan Bui, Dinh Q. Phung, Trung Le:
Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance. CoRR abs/2212.03069 (2022) - 2021
- [c75]Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Dinh Phung:
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness. AAAI 2021: 6831-6839 - [c74]Claudio Paoloni, Rupa Basu, Marcel Burhenn, Maruf Hossain, Daniel Huebsch, Viktor Krozer, Trung Le, Rosa Letizia, Ernesto Limiti, François Magne, Marc Marilier, Antonio Ramirez, Jeevan M. Rao, Giacomo Ulisse, Borja Vidal, Hadi Yacob:
Toward the first D-band Point to multipoint wireless system field test. EuCNC/6G Summit 2021: 55-59 - [c73]Trung Le, Zhao Zhang, Zhichun Zhu:
POMI: Polling-Based Memory Interface for Hybrid Memory System. ICCD 2021: 447-455 - [c72]Van-Anh Nguyen, Tuan Nguyen, Trung Le, Quan Hung Tran, Dinh Phung:
STEM: An approach to Multi-source Domain Adaptation with Guarantees. ICCV 2021: 9332-9343 - [c71]He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine:
Neural Topic Model via Optimal Transport. ICLR 2021 - [c70]Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung:
LAMDA: Label Matching Deep Domain Adaptation. ICML 2021: 6043-6054 - [c69]Tuan Nguyen, Trung Le, Nhan Dam, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung:
TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport. IJCAI 2021: 2862-2868 - [c68]Van Nguyen, Trung Le, Olivier Y. de Vel, Paul Montague, John Grundy, Dinh Phung:
Information-theoretic Source Code Vulnerability Highlighting. IJCNN 2021: 1-8 - [c67]Trung Phung, Trung Le, Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Q. Phung:
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources. NeurIPS 2021: 27720-27733 - [c66]Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, Dinh Q. Phung:
Most: multi-source domain adaptation via optimal transport for student-teacher learning. UAI 2021: 225-235 - [i28]Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Seyit Camtepe, Dinh Phung:
Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning. CoRR abs/2101.10027 (2021) - [i27]Khai Nguyen, Quoc Nguyen, Nhat Ho, Tung Pham, Hung Bui, Dinh Phung, Trung Le:
BoMb-OT: On Batch of Mini-batches Optimal Transport. CoRR abs/2102.05912 (2021) - [i26]Mahmoud Hossam, Trung Le, He Zhao, Viet Huynh, Dinh Phung:
Improved and Efficient Text Adversarial Attacks using Target Information. CoRR abs/2104.13484 (2021) - [i25]Mahmoud Hossam, Trung Le, Michael Papasimeon, Viet Huynh, Dinh Phung:
Text Generation with Deep Variational GAN. CoRR abs/2104.13488 (2021) - [i24]Trung Le, Ryan Poplin, Fred Bertsch, Andeep Singh Toor, Margaret L. Oh:
SyntheticFur dataset for neural rendering. CoRR abs/2105.06409 (2021) - [i23]Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, Dinh Q. Phung:
ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection. CoRR abs/2110.07317 (2021) - [i22]Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Valmiki Rampersad, Jens Axboe, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov, Chris Peterson, Mikhail Smelyanskiy, Vijay Rao:
Supporting Massive DLRM Inference Through Software Defined Memory. CoRR abs/2110.11489 (2021) - [i21]Trung Le, Dat Do, Tuan Nguyen, Huy Nguyen, Hung Bui, Nhat Ho, Dinh Q. Phung:
On Label Shift in Domain Adaptation via Wasserstein Distance. CoRR abs/2110.15520 (2021) - [i20]Trung Phung, Trung Le, Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Phung:
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources. CoRR abs/2111.13822 (2021) - 2020
- [c65]Quan Hung Tran, Nhan Dam, Tuan Manh Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung:
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering. COLING 2020: 5205-5210 - [c64]Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Q. Phung:
Improving Adversarial Robustness by Enforcing Local and Global Compactness. ECCV (27) 2020: 209-223 - [c63]Claudio Paoloni, Viktor Krozer, François Magne, Trung Le, Rupa Basu, Jeevan M. Rao, Rosa Letizia, Ernesto Limiti, Marc Marilier, Giacomo Ulisse, Antonio Ramirez, Borja Vidal, Hadi Yacob:
D-band Point to Multi-Point Deployment with G-Band Transport. EuCNC 2020: 84-88 - [c62]Quan Hoang, Trung Le, Dinh Phung:
Parameterized Rate-Distortion Stochastic Encoder. ICML 2020: 4293-4303 - [c61]Ngan Le, Trung Le, Kashu Yamazaki, Toan Duc Bui, Khoa Luu, Marios Savvides:
Offset Curves Loss for Imbalanced Problem in Medical Segmentation. ICPR 2020: 6189-6195 - [c60]Mahmoud Hossam, Trung Le, He Zhao, Dinh Phung:
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability. ICPR 2020: 8922-8928 - [c59]Nhan Dam, Trung Le, Viet Huynh, Dinh Phung:
Stein Variational Gradient Descent with Variance Reduction. IJCNN 2020: 1-8 - [c58]Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung:
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. IJCNN 2020: 1-8 - [c57]Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. de Vel, Paul Montague, Dinh Phung:
Code Pointer Network for Binary Function Scope Identification. IJCNN 2020: 1-7 - [c56]Tuan Nguyen, Trung Le, Khanh Nguyen, Olivier Y. de Vel, Paul Montague, John C. Grundy, Dinh Phung:
Deep Cost-Sensitive Kernel Machine for Binary Software Vulnerability Detection. PAKDD (2) 2020: 164-177 - [c55]Van Nguyen, Trung Le, Olivier Y. de Vel, Paul Montague, John C. Grundy, Dinh Phung:
Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection. PAKDD (1) 2020: 699-711 - [c54]Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. de Vel, Paul Montague, John C. Grundy, Dinh Phung:
Code Action Network for Binary Function Scope Identification. PAKDD (1) 2020: 712-725 - [i19]Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung:
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. CoRR abs/2004.07534 (2020) - [i18]Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Phung:
Improving Adversarial Robustness by Enforcing Local and Global Compactness. CoRR abs/2007.05123 (2020) - [i17]He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine:
Neural Sinkhorn Topic Model. CoRR abs/2008.13537 (2020) - [i16]Tuan-Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier Y. DeVel, Tamas Abraham, Dinh Phung:
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness. CoRR abs/2009.09612 (2020) - [i15]He Zhao, Trung Le, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Dinh Phung:
Towards Understanding Pixel Vulnerability under Adversarial Attacks for Images. CoRR abs/2010.06131 (2020) - [i14]Mahmoud Hossam, Trung Le, He Zhao, Dinh Phung:
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability. CoRR abs/2010.06812 (2020) - [i13]Quan Hung Tran, Nhan Dam, Tuan Manh Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung:
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering. CoRR abs/2011.03096 (2020) - [i12]Ngan Le, Trung Le, Kashu Yamazaki, Toan Duc Bui, Khoa Luu, Marios Savvides:
Offset Curves Loss for Imbalanced Problem in Medical Segmentation. CoRR abs/2012.02463 (2020)
2010 – 2019
- 2019
- [j8]Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
GoGP: scalable geometric-based Gaussian process for online regression. Knowl. Inf. Syst. 60(1): 197-226 (2019) - [c53]Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung:
Robust Anomaly Detection in Videos Using Multilevel Representations. AAAI 2019: 5216-5223 - [c52]Claudio Paoloni, Sebastian Boppel, Viktor Krozer, Trung Le, Rosa Letizia, Ernesto Limiti, François Magne, Marc Marilier, Antonio Ramirez, Borja Vidal, Ralph Zimmerman:
Technology for D-band/G-band ultra capacity layer. EuCNC 2019: 209-213 - [c51]Tue Le, Tuan Nguyen, Trung Le, Dinh Q. Phung, Paul Montague, Olivier Y. de Vel, Lizhen Qu:
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection. ICLR (Poster) 2019 - [c50]Nhan Dam, Quan Hoang, Trung Le, Tu Dinh Nguyen, Hung Bui, Dinh Phung:
Three-Player Wasserstein GAN via Amortised Duality. IJCAI 2019: 2202-2208 - [c49]Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Q. Phung:
Learning Generative Adversarial Networks from Multiple Data Sources. IJCAI 2019: 2823-2829 - [c48]Van Nguyen, Trung Le, Tue Le, Khanh Nguyen, Olivier Y. DeVel, Paul Montague, Lizhen Qu, Dinh Q. Phung:
Deep Domain Adaptation for Vulnerable Code Function Identification. IJCNN 2019: 1-8 - [i11]Trung Le, Dinh Q. Phung:
When Can Neural Networks Learn Connected Decision Regions? CoRR abs/1901.08710 (2019) - [i10]He Zhao, Trung Le, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Dinh Phung:
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions. CoRR abs/1910.01329 (2019) - 2018
- [c47]Khanh Nguyen, Nhan Dam, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung:
Clustering Induced Kernel Learning. ACML 2018: 129-144 - [c46]Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Q. Phung:
Batch Normalized Deep Boltzmann Machines. ACML 2018: 359-374 - [c45]Claudio Paoloni, François Magne, Frédéric André, Joel Willebois, Trung Le, Xavier Begaud, Giacomo Ulisse, Viktor Krozer, Rosa Letizia, Marc Marilier, Antonio Ramirez, Ralph Zimmerman:
Transmisson Hub and Terminals for Point to Multipoint W-Band Tweether System. EuCNC 2018: 1-9 - [c44]Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung:
MGAN: Training Generative Adversarial Nets with Multiple Generators. ICLR (Poster) 2018 - [c43]Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung:
Bayesian Multi-Hyperplane Machine for Pattern Recognition. ICPR 2018: 609-614 - [c42]Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung:
Geometric Enclosing Networks. IJCAI 2018: 2355-2361 - [c41]Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, Geoffrey I. Webb:
Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data. KDD 2018: 2003-2011 - [c40]Hung Nguyen, Van Nguyen, Thin Nguyen, Mark E. Larsen, Bridianne O'Dea, Duc Thanh Nguyen, Trung Le, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen:
Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web. WISE (2) 2018: 100-110 - [i9]Trung Le, Khanh Nguyen, Dinh Q. Phung:
Theoretical Perspective of Deep Domain Adaptation. CoRR abs/1811.06199 (2018) - 2017
- [j7]Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung:
Approximation Vector Machines for Large-scale Online Learning. J. Mach. Learn. Res. 18: 111:1-111:55 (2017) - [j6]Tung Pham, Hang Dang, Trung Le, Thai Hoang Le:
Fast support vector clustering. Vietnam. J. Comput. Sci. 4(1): 13-21 (2017) - [c39]Trung Le, Khanh Nguyen, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
GoGP: Fast Online Regression with Gaussian Processes. ICDM 2017: 257-266 - [c38]Tu Dinh Nguyen, Trung Le, Hung Bui, Dinh Q. Phung:
Large-scale Online Kernel Learning with Random Feature Reparameterization. IJCAI 2017: 2543-2549 - [c37]Vu Nguyen, Dinh Q. Phung, Trung Le, Hung Bui:
Discriminative Bayesian Nonparametric Clustering. IJCAI 2017: 2550-2556 - [c36]Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Q. Phung:
Dual Discriminator Generative Adversarial Nets. NIPS 2017: 2670-2680 - [c35]Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le:
Supervised Restricted Boltzmann Machines. UAI 2017 - [i8]Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung:
Multi-Generator Generative Adversarial Nets. CoRR abs/1708.02556 (2017) - [i7]Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Q. Phung:
Geometric Enclosing Networks. CoRR abs/1708.04733 (2017) - [i6]Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Q. Phung:
Dual Discriminator Generative Adversarial Nets. CoRR abs/1709.03831 (2017) - [i5]Trung Le, Khanh Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
Analogical-based Bayesian Optimization. CoRR abs/1709.06390 (2017) - [i4]Tung Pham, Trung Le, Hang Dang:
Scalable Support Vector Clustering Using Budget. CoRR abs/1709.06444 (2017) - [i3]Trung Le, Tu Dinh Nguyen, Dinh Q. Phung:
KGAN: How to Break The Minimax Game in GAN. CoRR abs/1711.01744 (2017) - 2016
- [j5]Duy Nguyen, Vinh Lai, Khanh Nguyen, Trung Le:
Mixture of hyperspheres for novelty detection. Vietnam. J. Comput. Sci. 3(4): 223-233 (2016) - [c34]Khanh Nguyen, Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
Multiple Kernel Learning with Data Augmentation. ACML 2016: 49-64 - [c33]Trung Le, Vu Nguyen, Tu Dinh Nguyen, Dinh Q. Phung:
Nonparametric Budgeted Stochastic Gradient Descent. AISTATS 2016: 654-572 - [c32]Tung Pham, Trung Le, Thai Hoang Le, Dat Tran:
Fast Support Vector Clustering. ESANN 2016 - [c31]Vu Nguyen, Tu Dinh Nguyen, Trung Le, Svetha Venkatesh, Dinh Q. Phung:
One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems. ICDM 2016: 1113-1118 - [c30]Tu Dinh Nguyen, Vu Nguyen, Trung Le, Dinh Q. Phung:
Distributed data augmented support vector machine on Spark. ICPR 2016: 498-503 - [c29]Anh Le, Trung Le, Khanh Nguyen, Van Nguyen, Thai Hoang Le, Dat Tran:
Fast Kernel-based method for anomaly detection. IJCNN 2016: 3211-3217 - [c28]Tuan Nguyen, Phuong Duong, Trung Le, Anh Le, Viet Ngo, Dat Tran, Wanli Ma:
Fuzzy Kernel Stochastic Gradient Descent machines. IJCNN 2016: 3226-3232 - [c27]Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung:
Dual Space Gradient Descent for Online Learning. NIPS 2016: 4583-4591 - [c26]Khanh Nguyen, Trung Le, Vu Nguyen, Dinh Q. Phung:
Sparse Adaptive Multi-hyperplane Machine. PAKDD (1) 2016: 27-39 - [c25]Trung Le, Phuong Duong, Mi Dinh, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung:
Budgeted Semi-supervised Support Vector Machine . UAI 2016 - [i2]Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung:
Approximation Vector Machines for Large-scale Online Learning. CoRR abs/1604.06518 (2016) - [i1]Trung Le, Khanh Nguyen, Van Nguyen, Vu Nguyen, Dinh Q. Phung:
Scalable Support Vector Machine for Semi-supervised Learning. CoRR abs/1606.06793 (2016) - 2015
- [j4]Trung Le, David Wright:
Scheduling workloads in a network of datacentres to reduce electricity cost and carbon footprint. Sustain. Comput. Informatics Syst. 5: 31-40 (2015) - [c24]Phuong Duong, Van Nguyen, Mi Dinh, Trung Le, Dat Tran, Wanli Ma:
Graph-based semi-supervised Support Vector Data Description for novelty detection. IJCNN 2015: 1-6 - [c23]Khanh Nguyen, Trung Le, Vinh Lai, Duy Nguyen, Dat Tran, Wanli Ma:
Least square Support Vector Machine for large-scale dataset. IJCNN 2015: 1-8 - [c22]Trung Le, Dinh Q. Phung, Khanh Nguyen, Svetha Venkatesh:
Fast One-Class Support Vector Machine for Novelty Detection. PAKDD (2) 2015: 189-200 - 2014
- [c21]Wanli Ma, Dat Tran, Trung Le, Hong Lin, Shang-Ming Zhou:
Using EEG artifacts for BCI applications. IJCNN 2014: 3628-3635 - [c20]Van Nguyen, Trung Le, Thien Pham, Mi Dinh, Thai Hoang Le:
Kernel-based semi-supervised learning for novelty detection. IJCNN 2014: 4129-4136 - [c19]Trung Le, Dat Tran, Wanli Ma, Thien Pham, Phuong Duong, Minh Nguyen:
Robust Support Vector Machine. IJCNN 2014: 4137-4144 - 2013
- [j3]Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma, Dharmendra Sharma:
Proximity multi-sphere support vector clustering. Neural Comput. Appl. 22(7-8): 1309-1319 (2013) - [c18]Trung Le, Dat Tran, Van Nguyen, Wanli Ma:
Maximal margin learning vector quantisation. IJCNN 2013: 1-6 - [c17]Trung Le, Dat Tran, Tien Tran, Khanh Nguyen, Wanli Ma:
Fuzzy entropy semi-supervised support vector data description. IJCNN 2013: 1-5 - [c16]Phuoc Nguyen, Dat Tran, Trung Le, Xu Huang, Wanli Ma:
EEG-Based Person Verification Using Multi-Sphere SVDD and UBM. PAKDD (1) 2013: 289-300 - [c15]Trung Le, Dat Tran, Wanli Ma:
Fuzzy Multi-Sphere Support Vector Data Description. PAKDD (2) 2013: 570-581 - 2012
- [c14]Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma:
Fuzzy Multi-sphere Support Vector Data Description. FUZZ-IEEE 2012: 1-5 - [c13]Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma:
Deterministic Annealing Multi-Sphere Support Vector Data Description. ICONIP (3) 2012: 183-190 - [c12]Trung Le, Dat Tran, Tuan Hoang, Dharmendra Sharma:
Maximal Margin Approach to Kernel Generalised Learning Vector Quantisation for Brain-Computer Interface. ICONIP (3) 2012: 191-198 - [c11]Tuan Hoang, Dat Tran, Khoa Truong, Trung Le, Xu Huang, Dharmendra Sharma, Toi Vo:
Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems. ICONIP (2) 2012: 192-201 - [c10]Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma:
A unified model for support vector machine and support vector data description. IJCNN 2012: 1-8 - 2011
- [j2]Brandon Chisham, Ben Wright, Trung Le, Tran Cao Son, Enrico Pontelli:
CDAO-Store: Ontology-driven Data Integration for Phylogenetic Analysis. BMC Bioinform. 12: 98 (2011) - [c9]Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma:
A Novel Parameter Refinement Approach to One Class Support Vector Machine. ICONIP (2) 2011: 529-536 - [c8]Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma, Dharmendra Sharma:
Multi-Sphere Support Vector Clustering. ICONIP (2) 2011: 537-544 - [c7]Trung Le, Dat Tran, Tuan Hoang, Wanli Ma, Dharmendra Sharma:
Generalised Support Vector Machine for Brain-Computer Interface. ICONIP (1) 2011: 692-700 - [c6]Trung Le, Dat Tran, Phuoc Nguyen, Wanli Ma, Dharmendra Sharma:
Multiple distribution data description learning method for novelty detection. IJCNN 2011: 2321-2326 - [c5]Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma:
Multiple Distribution Data Description Learning Algorithm for Novelty Detection. PAKDD (2) 2011: 246-257 - 2010
- [j1]Tuan Hoang, Phuoc Nguyen, Trung Le, Dat Tran, Dharmendra Sharma:
Enhancing Performance of SVM-Based Brain-Computer Interface Systems. Aust. J. Intell. Inf. Process. Syst. 11(3) (2010) - [c4]Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma:
A new support vector machine method for medical image classification. EUVIP 2010: 165-170 - [c3]Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma:
A Theoretical Framework for Multi-sphere Support Vector Data Description. ICONIP (2) 2010: 132-142 - [c2]Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma:
An optimal sphere and two large margins approach for novelty detection. IJCNN 2010: 1-6 - [c1]Phuoc Nguyen, Trung Le, Dat Tran, Xu Huang, Dharmendra Sharma:
Fuzzy support vector machines for age and gender classification. INTERSPEECH 2010: 2806-2809
Coauthor Index
[j20] [j19] [j18] [c104] [c103] [c101] [c100] [c99] [i69] [i68] [i67] [i66] [i65] [i64] [i63] [i60] [i59] [i58] [j14] [j12] [c98] [c95] [c94] [c93] [c92] [c89] [c88] [c87] [c86] [i57] [i56] [i55] [i54] [i53] [i52] [i51] [i50] [i49] [i48] [i47] [i46] [i43] [i42] [i41] [j10] [j9] [c85] [c84] [c82] [c81] [c80] [c78] [c77] [c76] [i40] [i39] [i38] [i36] [i35] [i34] [i33] [i32] [i31] [i30] [i29] [c75] [c72] [c71] [c70] [c69] [c68] [c67] [c66] [i28] [i27] [i26] [i25] [i23] [i21] [i20] [c65] [c64] [c62] [c60] [c59] [c58] [c57] [c56] [c55] [c54] [i19] [i18] [i17] [i16] [i15] [i14] [i13] [j8] [c53] [c51] [c50] [c49] [c48] [i11] [i10] [c47] [c46] [c44] [c43] [c42] [c41] [c40] [i9] [j7] [c39] [c38] [c37] [c36] [c35] [i8] [i7] [i6] [i5] [i3] [c34] [c33] [c31] [c30] [c27] [c26] [c25] [i2] [i1] [c22]
Chakkrit Tantithamthavorn
aka: Chakkrit Kla Tantithamthavorn
aka: Chakkrit Kla Tantithamthavorn
Olivier Y. de Vel
aka: Olivier Y. DeVel
aka: Olivier Y. DeVel
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