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Pieter Abbeel; Professor of Electrical Engineering and Computer Science; University of California, Berkeley Unsupervised Reinforcement Learning Deep reinforcement learning (Deep RL) has seen many successes, including learning to play Atari games, the classical game of Go, robotic locomotion and manipulation. However, past successes are ultimately in fairly narrow problem domains...

Mihaela van der Schaar, John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge; Turing Fellow, The Alan Turing Institute in London; Chancellor鈥檚 Professor, UCLA Why medicine is creating exciting new frontiers for machine learning Medicine stands apart from other areas where machine learning can be...

Abdelrahman Mohamed, Research Scientist, Facebook AI Research Recent advances in speech representation learning Self-supervised representation learning methods recently achieved great successes in NLP and computer vision domains, reaching new performance levels while reducing required labels for many downstream scenarios. Speech representation learning is experiencing similar progress, with work primarily focused...

Oriol Vinyals, Research Scientist, Google DeepMind AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning Games have been used for decades as an important way to test and evaluate the performance of artificial intelligence systems. As capabilities have increased, the research community has sought games with increasing complexity that...

Yanping Huang, Staff Software Engineer, Google Brain GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and computation. Although this trend of scaling is affirmed to...

Fausto Milletari, Lead of Applied AI, Johnson and Johnson Volumetric medical image processing with deep learning One of the fundamental capabilities of deep learning is its ability of accomplishing a multitude of tasks by learning features directly from raw data instead of relying on a fixed set of features purposefully...

Muyinatu A. Lediju Bell, Assistant Professor of Electrical and Computer Engineering, Biomedical Engineering, and Computer Science, Johns Hopkins University Ultrasound Image Formation in the Deep Learning Age The success of diagnostic and interventional medical procedures is deeply rooted in the ability of modern imaging systems to deliver clear and interpretable...

Andrey Zhmoginov, Research Software Engineer, Google AI Image understanding and image-to-image translation through the lens of information loss The computation performed by a deep neural network is typically composed of multiple processing stages during which the information contained in the model input gradually 鈥渄issipates鈥� as different areas of the input...

Sanja Fidler, Department of Computer Science, University of Toronto; and Director of AI, NVIDIA corporation Towards AI for 3D Content Creation 3D content is key in several domains such as architecture, film, gaming, and robotics. However, creating 3D content can be very time consuming -- the artists need to sculpt...

Ruslan Salakhutdinov, UPMC Professor of Computer Science, Department of Machine Learning, Carnegie Mellon University Integrating Domain-Knowledge into Deep Learning. In this talk I will first discuss deep learning models that can find semantically meaningful representations of words, learn to read documents and answer questions about their content. I will introduce...