1 code implementation • 25 May 2023 • Vy Vo, Trung Le, Tung-Long Vuong, He Zhao, Edwin Bonilla, Dinh Phung
Estimating the parameters of a probabilistic directed graphical model from incomplete data is a long-standing challenge.
no code implementations • 12 Feb 2023 • Tung-Long Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Phung
Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks.
no code implementations • 25 Sep 2019 • Tung-Long Vuong, Han Nguyen, Hai Pham, Kenneth Tran
Under this framework, the objective function can represented end-to-end as a single computational graph, which allows seamless policy gradient computation via backpropagation through the models.
no code implementations • 25 Jun 2019 • Tung-Long Vuong, Kenneth Tran
Model-based reinforcement learning has the potential to be more sample efficient than model-free approaches.