[Colloquium] TTIC Talk: Honglak Lee, Stanford

[Julia MacGlashan]

*REMINDER*

When:             *Monda**y, Apr 19 @ 11:00am
*

Where:           * TTIC Conference Room #526*, 6045 S Kenwood Ave, 5th Floor


Who:              * **Honglak Lee*, Stanford


Title:          *      **Unsupervised Feature Learning*****



 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.

BIO:

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.

Host:              Nati Srebro, nati at ttic.edu
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