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Faculty Candidate Seminar

CSE Fac Candidate, Honglak Lee

Unsupervised Feature LearningPhD Student, Research AssistantStanford University
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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, I will present machine learning algorithms that can
automatically learn good feature representations from unlabeled data
in various domains, such as images, audio, text, and robotic sensors.
Specifically, I will first describe how "sparse coding" algorithms —
which represent each input example using a small number of basis
vectors — can be used to learn good low-level representations from
unlabeled data. I also show that this gives feature representations
that yield improved performance in object recognition, audio
classification, text classification, and 3D point cloud
classification. In addition, I will present an algorithm for building
more complex, hierarchical representations, in which more complex
features are automatically learned as a composition of simpler ones.
When applied to images, this method automatically learns features that
correspond to objects and decompositions of objects into object-parts.
These features often lead to performance competitive with or better
than highly hand-engineered computer vision algorithms in object
recognition and image segmentation tasks. Further, the same algorithm
can be used to learn feature representations from audio data. Here,
the learned features yield improved performance over state-of-the-art
methods in several different speech recognition tasks, such as speaker
identification, phone classification, and gender classification.
Honglak Lee is a Ph.D. candidate in Computer Science Department at
Stanford University, where he is advised by Andrew Ng. His research
interests include machine learning, artificial intelligence, and data
mining. He received ICML 2009 best application paper award and CEAS
2005 best student paper award. Honglak graduated with a B.Sc. in
Physics and Computer Science from Seoul National University in Korea.
He has been a recipient of Korea Foundation of Advanced Studies
Fellowship and Stanford Graduate Fellowship.

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