Toyota AI Seminar: Unsupervised Feature Learning via Sparse Hierarchical Representations
University of Michigan, CSE
Tuesday, October 12, 2010|
4:00pm - 5:30pm
3725 Beyster Bldg. (Stained Glass Conference Room)
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About the Event
Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good feature representation of the data, and having a poor representation can severely limit the performance of learning algorithms. These feature representations are often hand-designed, require significant amounts of domain knowledge and human labor, and do not generalize well to new domains. To address these issues, there has been much interest in algorithms that learn feature hierarchies from unlabeled data. In this talk, I will discuss the fundamental challenges and talk about my current and future work in developing machine learning algorithms that can learn invariant representations from unlabeled and labeled data.
Honglak Lee is an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. His research focuses on machine learning and its application across a broad range of perception challenges, from computer vision and robotics to speech recognition and natural language processing. His research is aimed at developing algorithms for unsupervised and semi-supervised learning of hierarchical features for artificial intelligence and large-scale data mining applications. Other areas of interest include supervised learning, probabilistic graphical models, convex optimization, and high-dimensional data analysis. He obtained his Ph.D. in Computer Science from Stanford University and has received ICML 2009 best application paper award and CEAS 2005 best student paper award.
Contact: Quang Duong
Open to: Public