Bayesian deep learning
Bayesian models are rooted in Bayesian statistics and easily benefit from the vast literature in the field. In contrast, deep learning lacks a solid mathematical grounding. Instead, empirical developments in deep learning are often justified by metaphors, evading the unexplained principles at play. These two fields are perceived as fairly antipodal to each other in their respective communities. It is perhaps astonishing then that most modern deep learning models can be cast as performing approximate inference in a Bayesian setting. The implications of this are profound: we can use the rich Bayesian statistics literature with deep learning models, explain away many of the curiosities with ad hoc techniques, combine results from deep learning into Bayesian modelling, and much more. In this talk, Yarin Gal will discuss interesting advances in the field.
WHAT IS TRUSTWORTHY AI SERIES?
Artificial Intelligence (AI) systems have steadily grown in complexity, gaining predictivity often at the expense of interpretability, robustness and trustworthiness. Deep neural networks are a prime example of this development. While reaching “superhuman” performances in various complex tasks, these models are susceptible to errors when confronted with tiny (adversarial) variations of the input – variations which are either not noticeable or can be handled reliably by humans. This expert talk series will discuss these challenges of current AI technology and will present new research aiming at overcoming these limitations and developing AI systems which can be certified to be trustworthy and robust.
The expert talk series will cover the following topics:
- Measuring Neural Network Robustness
- Auditing AI Systems
- Adversarial Attacks and Defences
- Explainability & Trustworthiness
- Poisoning Attacks on AI
- Certified Robustness
- Model and Data Uncertainty
- AI Safety and Fairness
The Trustworthy AI series is moderated by Wojciech Samek, Head of AI Department at Fraunhofer HHI, one of the top 20 AI labs in the world.