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SyntheticFur dataset for neural rendering


May 13, 2021
Trung Le, Ryan Poplin, Fred Bertsch, Andeep Singh Toor, Margaret L. Oh


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Improved and Efficient Text Adversarial Attacks using Target Information


May 02, 2021
Mahmoud Hossam, Trung Le, He Zhao, Viet Huynh, Dinh Phung

* Accepted in the International Conference on Learning Representations (ICLR) workshop on Robust and Reliable Machine Learning in the Real World (RobustML) 

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Text Generation with Deep Variational GAN


Apr 27, 2021
Mahmoud Hossam, Trung Le, Michael Papasimeon, Viet Huynh, Dinh Phung

* Accepted in the Third Workshop on Bayesian Deep Learning (NIPS / NeurIPS 2018) 

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


Feb 11, 2021
Khai Nguyen, Quoc Nguyen, Nhat Ho, Tung Pham, Hung Bui, Dinh Phung, Trung Le

* 36 pages, 18 figures 

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Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning


Jan 25, 2021
Anh Bui, Trung Le, He Zhao, Paul Montague, Seyit Camtepe, Dinh Phung


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Offset Curves Loss for Imbalanced Problem in Medical Segmentation


Dec 04, 2020
Ngan Le, Trung Le, Kashu Yamazaki, Toan Duc Bui, Khoa Luu, Marios Savides

* ICPR 2020 

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


Nov 05, 2020
Quan Tran, Nhan Dam, Tuan Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung

* Accepted to COLING 2020 

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Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability


Oct 21, 2020
Mahmoud Hossam, Trung Le, He Zhao, Dinh Phung

* Preprint for accepted paper at 25th International Conference on Pattern Recognition (ICPR 2020) 

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Transfer2Attack: Text Adversarial Attacks via Cross-Domain Interpretability


Oct 19, 2020
Mahmoud Hossam, Trung Le, He Zhao, Dinh Phung

* Preprint for accepted paper at 25th International Conference on Pattern Recognition (ICPR 2020) 

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Towards Understanding Pixel Vulnerability under Adversarial Attacks for Images


Oct 13, 2020
He Zhao, Trung Le, Paul Montague, Olivier De Vel, Tamas Abraham, Dinh Phung


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Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness


Sep 21, 2020
Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier deVel, Tamas Abraham, Dinh Phung


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Neural Sinkhorn Topic Model


Aug 12, 2020
He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray Buntine


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Improving Adversarial Robustness by Enforcing Local and Global Compactness


Jul 10, 2020
Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier deVel, Tamas Abraham, Dinh Phung

* Proceeding of the European Conference on Computer Vision (ECCV) 2020 

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OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation


May 22, 2020
Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung

* Preprint for accepted conference paper at International Joint Conference on Neural Networks (IJCNN) 2020 

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Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions


Oct 03, 2019
He Zhao, Trung Le, Paul Montague, Olivier De Vel, Tamas Abraham, Dinh Phung


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When Can Neural Networks Learn Connected Decision Regions?


Jan 25, 2019
Trung Le, Dinh Phung


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Theoretical Perspective of Deep Domain Adaptation


Nov 15, 2018
Trung Le, Khanh Nguyen, Dinh Phung


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KGAN: How to Break The Minimax Game in GAN


Nov 06, 2017
Trung Le, Tu Dinh Nguyen, Dinh Phung


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Multi-Generator Generative Adversarial Nets


Oct 27, 2017
Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung


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Scalable Support Vector Clustering Using Budget


Sep 19, 2017
Tung Pham, Trung Le, Hang Dang


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Analogical-based Bayesian Optimization


Sep 19, 2017
Trung Le, Khanh Nguyen, Tu Dinh Nguyen, Dinh Phung


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Dual Discriminator Generative Adversarial Nets


Sep 12, 2017
Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Phung


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Geometric Enclosing Networks


Aug 17, 2017
Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Phung


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Approximation Vector Machines for Large-scale Online Learning


May 28, 2017
Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung

* 54 pages 

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Scalable Semi-supervised Learning with Graph-based Kernel Machine


Apr 06, 2017
Trung Le, Khanh Nguyen, Van Nguyen, Vu Nguyen, Dinh Phung

* 21 pages 

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