Trustworthy AI: 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.
Speakers, Panelists and Moderators
YARIN GALAssociate Professor of Machine LearningUniversity of OxfordI am an Associate Professor of Machine Learning at the University of Oxford Computer Science department, and head of the Oxford Applied and Theoretical Machine Learning Group (OATML). I am also the Tutorial Fellow in Computer Science at Christ Church, Oxford, and Fellow at the Alan Turing Institute, the UK's national institute for data science. Prior to my move to Oxford I was a Research Fellow in Computer Science at St Catharine's College at the University of Cambridge. I obtained my PhD from the Cambridge machine learning group, working with Prof Zoubin Ghahramani and funded by the Google Europe Doctoral Fellowship. Prior to that I studied at Oxford Computer Science department for a Master's degree under the supervision of Prof Phil Blunsom. Before my MSc I worked as a software engineer for 3 years at IDesia Biometrics developing code and UI for mobile platforms, and did my undergraduate in mathematics and computer science at the Open University in Israel.
WOJCIECH SAMEKHead of Department of Artificial IntelligenceFraunhofer Heinrich Hertz InstituteWojciech Samek is head of the Department of Artificial Intelligence and the Explainable AI Group at Fraunhofer Heinrich Hertz Institute (HHI), Berlin, Germany. The Fraunhofer Heinrich Hertz Institute (HHI) is ranked among top 20 Artificial Intelligence Research Labs in the world. He studied computer science at Humboldt University of Berlin, Heriot-Watt University and University of Edinburgh from 2004 to 2010 and received the Dr. rer. nat. degree with distinction (summa cum laude) from the Technical University of Berlin in 2014. During his studies he was awarded scholarships from the German Academic Scholarship Foundation and the DFG Research Training Group GRK 1589/1, and was a visiting researcher at NASA Ames Research Center, Mountain View, USA. After his PhD he founded the Machine Learning Group at Fraunhofer HHI, which he has directed until 2020. Dr. Samek is associated faculty at the Berlin Institute for the Foundation of Learning and Data (BIFOLD), the ELLIS Unit Berlin and the DFG Graduate School BIOQIC. Furthermore, he is an editorial board member of PLoS ONE, Pattern Recognition and IEEE TNNLS and an elected member of the IEEE MLSP Technical Committee. He is recipient of multiple best paper awards, including the 2020 Pattern Recognition Best Paper Award, and part of the MPEG-7 Part 17 standardization. He is co-editor of the Springer book "Explainable AI: Interpreting, Explaining and Visualizing Deep Learning" and has organized various special sessions, workshops and tutorials on topics such as explainable AI, neural network compression, and federated learning. Dr. Samek has co-authored more than 150 peer-reviewed journal and conference papers; some of them listed by Thomson Reuters as "Highly Cited Papers" (i.e., top 1%) in the field of Engineering.