It is a research group under the Stanford Vision & Learning Lab that focuses on developing methods and mechanisms for generalizable robot perception and control.
We work on challenging open problems at the intersection of computer vision, machine learning, and robotics. We develop algorithms and systems that unify in reinforcement learning, control theoretic modeling, and 2D/3D visual scene understanding to teach robots to perceive and to interact with the physical world.
While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes:
- goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster
- meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly
- curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer
This is a graduate-level course. By the end of the course, students will be able to understand and implement the state-of-the-art multi-task learning and meta-learning algorithms and be ready to conduct research on these topics.
“Are you in favor of 75,000 suicides and tens of millions of starving children across the earth? Either face the problem of lifting the lockdown or you
Learn AI from Stanford professors Christopher Manning, Andrew Ng, and Emma Brunskill. Free online course videos in Deep Learning, Reinforcement Learning, and Natural Language Processing.
K. Toutanova, D. Klein, C. Manning, and Y. Singer. NAACL '03: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, page 173--180. Morristown, NJ, USA, Association for Computational Linguistics, (2003)
T. Grenager, and C. Manning. Proceedings of the Conference on Empirical Methods in Natural Language Processing, The Stanford Natural Language Processing Group, (2006)
T. Grenager, and C. Manning. Proceedings of the Conference on Empirical Methods in Natural Language Processing, The Stanford Natural Language Processing Group, (2006)
R. Snow, D. Jurafsky, and A. Ng. Proceedings of the 44 th Annual Meeting of the Association for Computational Linguistics, The Stanford Natural Language Processing Group, (2006)Received Best Paper Award.
W. Morgan, P. Chang, S. Gupta, and J. Brenier. Proceedings of the 7 th SIGdial Workshop on Discourse and Dialogue, The Stanford Natural Language Processing Group, (2006)
R. Swanson, and A. Gordon. Proceedings of the Joint Conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics, page 17-21. Sydney, Australia, (July 2006)