- 18:30: Doors open, pizza, beer, networking
- 19:00: First talk
- 20:00: Break & networking
- 20:15: Second talk
- 21:30: Close
* Is recognition enough to learn how to see? - Alex Kendall
Computer vision has provided a challenging setting for machine learning research. Problems like ImageNet recognition have driven advances in deep learning over the last few years. However, in this talk I'm going to argue that learning to see requires so much more than recognition - which ImageNet classification models provide. For example, mobile robots such as autonomous vehicles need to know the geometry of the scene around them, the motion of the scene and predict the trajectories of other agents. I will explain how to formulate deep learning models to understand scene geometry and semantics using examples from my research. In addition, I will discuss recent advances in Bayesian deep learning which allows us to quantify our model's uncertainty and learn to see in data-efficient ways.
Bio: Alex Kendall co-founded and is CTO at Wayve Technologies. He also holds a Research Fellowship at Trinity College at the University of Cambridge. He graduated with a Bachelor of Engineering from the University of Auckland, New Zealand. In 2014, he was awarded a Woolf Fisher Scholarship to study towards a Ph.D. at the University of Cambridge. Alex’s research investigates applications of deep learning for robot perception and control. His technology has been used to power smart-city infrastructure with Vivacity, control self-driving cars with Toyota Research Institute and enable next-generation drone flight with Skydio.
* (Some) Artificial Intelligence Frontiers - Oriol Vinyals
In this talk I'll describe three select challenges which are actively researched in our community, yet still elusive in supervised, unsupervised, and reinforcement learning respectively. In supervised learning, learning new concepts quickly is still far from solved, however research directions such as meta learning recently brought us some exciting advances. Despite impressive samples from unsupervised learning, meaningful metrics are still needed in order to assess progress and utility of generative models. And lastly, RL proved successful in e.g. beating the world champion of Go, but finding or building environments that are realistic and meaningful remains challenging.
Bio: Oriol Vinyals is a Sr Staff Research Scientist at Google DeepMind, working in Deep Learning. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award. His research has been featured multiple times at the New York Times, BBC, etc., and his articles have been cited over 15000 times. At DeepMind he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, deep learning and reinforcement learning.