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Deep Learning Stage [clear filter]
Thursday, January 24


Beyond Supervised Driving
Crowd-sourced steering does not sound as appealing as automated driving. We need to go beyond supervised learning for automated driving, including for computer vision problems seeing great progress with strong supervision today. First, we will motivate exciting scientific problems that have huge implications in the research and development of long-term large-scale autonomous robots, such as unsupervised domain adaptation, self-supervised learning, and robustness to edge cases. Second, we will talk about the robotics system perspective, especially end-to-end vs modular design and human-robot interaction. Finally, we will describe some of TRI's related research directions, especially around world-scale cloud robotics. In particular, we will discuss recent related results obtained in the ML team at TRI on state-of-the-art methods for self-supervised depth and pose prediction from monocular imagery, end-to-end panoptic segmentation, and large scale distributed deep learning on GPUs in the cloud.

avatar for Adrien Gaidon, Toyota Research Institute

Adrien Gaidon, Toyota Research Institute

Machine Learning Lead & Snr. Research Scientist, Toyota Research Institute
Adrien Gaidon is the Manager of the Machine Learning team and a Senior Research Scientist at the Toyota Research Institute (TRI) in Los Altos, CA, USA, working on open problems in world-scale learning for autonomous driving. He received his PhD from Microsoft Research - Inria Paris... Read More →

Thursday January 24, 2019 9:15am - 9:35am
Grand Ballroom Hyatt Regency San Francisco, 5 Embarcadero Center, San Francisco, CA 94111, USA


Object-oriented Perception and Control
Why are infants better at sensory motor tasks than our current AI systems? We are born with learning mechanisms to map our sensory experience into objects and abstractions over them. My talk will present unsupervised approaches integrating deep/reinforcement learning and probabilistic programming, to learn about objects and goal-directed control grounded in them. I will give demonstrations in a few domains including video understanding, game playing and robotics.

avatar for Tejas Kulkarni, DeepMind

Tejas Kulkarni, DeepMind

Research Scientist, DeepMind
I am a Research Scientist at Google DeepMind. Previously, I was a PhD student at MIT under the supervision of Joshua Tenenbaum. I am primarily interested in understanding how the mind works. My current research goal is to build learning algorithms that acquire grounded common-sense... Read More →

Thursday January 24, 2019 9:35am - 10:00am
Grand Ballroom Hyatt Regency San Francisco, 5 Embarcadero Center, San Francisco, CA 94111, USA


Adversarial Machine Learning
Most machine learning algorithms are based on optimization: given a cost function, the algorithm adapts the parameters to reduce the cost. Adversarial machine learning is instead based on game theory: multiple "players" compete to each reduce their own cost, often at the expense of other players. In this talk I show how adversarial machine learning is related to many active machine learning research areas.

avatar for Ian Goodfellow, Google Brain

Ian Goodfellow, Google Brain

Staff Research Scientist, Google Brain
Ian Goodfellow is a Staff Research Scientist at Google Brain. He is the lead author of the MIT Press textbook Deep Learning. In addition to generative models, he also studies security and privacy for machine learning. He has contributed to open source libraries including TensorFlow... Read More →

Thursday January 24, 2019 10:00am - 10:25am
Grand Ballroom Hyatt Regency San Francisco, 5 Embarcadero Center, San Francisco, CA 94111, USA