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2020 – today
- 2023
[c14]Viet Huynh, Buser Say, Peter Vogel, Lucy Cao, Geoffrey I. Webb, Aldeida Aleti:
Rapid Identification of Protein Formulations with Bayesian Optimisation. ICMLA 2023: 776-781- 2021
- [j2]Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung:
On efficient multilevel Clustering via Wasserstein distances. J. Mach. Learn. Res. 22: 145:1-145:43 (2021) - [c13]He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine
:
Neural Topic Model via Optimal Transport. ICLR 2021 - [c12]Viet Huynh, Dinh Q. Phung, He Zhao:
Optimal Transport for Deep Generative Models: State of the Art and Research Challenges. IJCAI 2021: 4450-4457 - [c11]He Zhao, Dinh Q. Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. Buntine:
Topic Modelling Meets Deep Neural Networks: A Survey. IJCAI 2021: 4713-4720 - [i8]He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. Buntine:
Topic Modelling Meets Deep Neural Networks: A Survey. CoRR abs/2103.00498 (2021) - [i7]Mahmoud Hossam, Trung Le, He Zhao, Viet Huynh, Dinh Phung:
Improved and Efficient Text Adversarial Attacks using Target Information. CoRR abs/2104.13484 (2021) - [i6]Mahmoud Hossam, Trung Le, Michael Papasimeon, Viet Huynh, Dinh Phung:
Text Generation with Deep Variational GAN. CoRR abs/2104.13488 (2021) - 2020
- [c10]Nhan Dam, Trung Le, Viet Huynh, Dinh Phung:
Stein Variational Gradient Descent with Variance Reduction. IJCNN 2020: 1-8 - [c9]Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung:
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. IJCNN 2020: 1-8 - [c8]Viet Huynh, He Zhao, Dinh Phung:
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling. NeurIPS 2020 - [i5]Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung:
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation. CoRR abs/2004.07534 (2020) - [i4]He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray L. Buntine:
Neural Sinkhorn Topic Model. CoRR abs/2008.13537 (2020)
2010 – 2019
- 2019
- [c7]Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. AISTATS 2019: 3149-3157 - [i3]Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung:
On Efficient Multilevel Clustering via Wasserstein Distances. CoRR abs/1909.08787 (2019) - [i2]Tam Le, Viet Huynh, Nhat Ho, Dinh Q. Phung, Makoto Yamada:
On Scalable Variant of Wasserstein Barycenter. CoRR abs/1910.04483 (2019) - 2018
- [i1]Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. CoRR abs/1810.11911 (2018) - 2017
- [j1]Viet Huynh, Dinh Q. Phung:
Streaming clustering with Bayesian nonparametric models. Neurocomputing 258: 52-62 (2017) - [c6]Nhan Dam, Dinh Q. Phung, Ba-Ngu Vo, Viet Huynh:
Forward-Backward Smoothing for Hidden Markov Models of Point Pattern Data. DSAA 2017: 252-261 - [c5]Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung:
Multilevel Clustering via Wasserstein Means. ICML 2017: 1501-1509 - [c4]Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le:
Supervised Restricted Boltzmann Machines. UAI 2017 - 2016
- [c3]Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui:
Scalable Nonparametric Bayesian Multilevel Clustering. UAI 2016 - 2015
- [c2]Viet Huynh, Dinh Q. Phung, Svetha Venkatesh:
Streaming Variational Inference for Dirichlet Process Mixtures. ACML 2015: 237-252 - [c1]Viet Huynh, Dinh Q. Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Hai Bui:
Learning Conditional Latent Structures from Multiple Data Sources. PAKDD (1) 2015: 343-354
Coauthor Index
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