Towards calibrated and flexible probabilistic deep learning


Thang Bui

University of Sydney
Fri, Jul 24 2020 - 03:00 pm (GMT + 7)
About Speaker

Thang Bui is a research scientist at Uber AI and a lecturer in Machine Learning at the University of Sydney. He has a PhD degree in Machine Learning from the Department of Engineering, University of Cambridge and a BEng from the University of Adelaide. He is broadly interested in machine learning and statistics, with a particular focus on neural networks, probabilistic models, approximate Bayesian inference, and sequential decision making under uncertainty.


Deep learning has achieved great successes in many real-world domains, ranging from vision, language to game playing. Yet, it has been shown to possess many limitations, including: (i) it is not robust to out-of-distribution inputs and (ii) it suffers from catastrophic forgetting when faced with streaming data. In this talk, I will show how we have addressed some of these limitations by combining deep learning with probabilistic modelling. This combination provides desirable test-time uncertainty estimates on out-of-distribution data and allows neural networks to be trained in an incremental way. If time permits, I will show general distributed learning, also known as federated learning, can also be handled by the same algorithmic framework.

Related seminars

Trieu Trinh

Google Deepmind

AlphaGeometry: Solving IMO Geometry without Human Demonstrations
Fri, Jul 5 2024 - 10:00 am (GMT + 7)

Tat-Jun (TJ) Chin

Adelaide University

Quantum Computing in Computer Vision: A Case Study in Robust Geometric Optimisation
Fri, Jun 7 2024 - 11:00 am (GMT + 7)

Fernando De la Torre

Carnegie Mellon University

Human Sensing for AR/VR
Wed, Apr 24 2024 - 07:00 am (GMT + 7)

Anh Nguyen

Microsoft GenAI

The Revolution of Small Language Models
Fri, Mar 8 2024 - 02:30 pm (GMT + 7)