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Variational Inference in Generative Models

less than 1 minute read

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Variational Inference (VI) first starts as a handy tool in Bayesian inference to approximate intractable posterior. Now, its usage goes beyond Bayesian inference, and we can see VI everywhere in classic learning, and deep learning-anywhere needs an approximation. After this lecture, we should: Read more

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preprints

publications

Universal Graph Continual Learning

Published/Accepted at Transactions on Machine Learning Research (TMLR) , 2023

Authors: Thanh Duc Hoang, Do Viet Tung, Duy-Hung Nguyen, Bao-Sinh Nguyen, Huy Hoang Nguyen, and Hung Le.
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talks

teaching

Teaching Assistant

Undergraduate course, Deakin University, 2018

SIT-112, Data Science Concepts. Spring, 2018.

Associate Supervisor

PhD course, Deakin University, 2021

S913, PhD. Candidate Bao Duong Nguyen. Causal Reasoning. 2021-2024

Associate Supervisor

PhD course, Deakin University, 2021

S913, PhD. Candidate Ragja Palakkadavath. Domain Generalization. 2022-2025

Associate Supervisor

PhD course, Deakin University, 2021

S913, PhD. Candidate Kha Pham. A theoretical investigation on neural memory. 2021-2024

Principle Supervisor

PhD course, Deakin University, 2023

F975, PhD. Candidate Minh Hoang Nguyen.Causal Reinforcement Learning, The Synergistic Relationship between Causal Inference and Reinforcement Learning. 2023-2026

Principle Supervisor

PhD course, Deakin University, 2023

F975, PhD. Candidate Van Dai Do. Efficient And Safe Large Language Models With Reinforcement Learning. 2023-2026