Statistical Learning Theory
09810CS 565300 -- Autumn 2009
Tuesday 13:10-15:00 & Thursday 13:10-14:00
234 CS Building
About the Term Projects
Each group consists of 1-3 members.
You may apply machine learning to to your research problems (supposedly involving large data sets), or study and implement one of the following papers.
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Unsupervised Detection of Regions of Interest Using Iterative Link Analysis
G. Kim, A. Torralba
[
pdf]
The "tree-dependent components" of natural scenes are edge filters
D. Zoran, Y. Weiss
[
pdf]
Structured output regression for detection with partial truncation
A. Vedaldi, A. Zisserman
[
pdf]
Speaker Comparison with Inner Product Discriminant Functions
W. Campbell, Z. Karam, D. Sturim
[
pdf]
Semi-Supervised Learning in Gigantic Image Collections
R. Fergus, Y. Weiss, A. Torralba
[
pdf]
Segmenting Scenes by Matching Image Composites
B. Russell, A. Efros, J. Sivic, B. Freeman, A. Zisserman
[
pdf]
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization
J. Wright, A. Balasubramanian, S. Rao, Y. Peng, Y. Ma
[
pdf]
Region-based Segmentation and Object Detection
S. Gould, T. Gao, D. Koller
[
pdf]
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions
P. Ram, D. Lee, H. Ouyang, A. Gray
[
pdf]
Positive Semidefinite Metric Learning with Boosting
C. Shen, J. Kim, L. Wang, A. van den Hengel
[
pdf]
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation
L. Du, L. Ren, D. Dunson, L. Carin
[
pdf]
A Biologically Plausible Model for Rapid Natural Scene Identification
S. Ghebreab, H. Steven, V. Lamme, A. Smeulders
[
pdf]
Perceptual Multistability as Markov Chain Monte Carlo Inference
S. Gershman, E. Vul, J. Tenenbaum
[
pdf]
[
slide]
On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation
S. Jagarlapudi, d. govindaraj, R. S, C. Bhattacharyya, A. Ben-Tal, K. Ramakrishnan
[
pdf]
On Learning Rotations
R. Arora
[
pdf]
[
slide]
Occlusive Components Analysis
J. Lucke, R. Turner, M. Sahani, M. Henniges
[
pdf]
[
slide]
Nonparametric Bayesian Texture Learning and Synthesis
L. Zhu, Y. Chen, B. Freeman, A. Torralba
[
pdf]
Modelling Relational Data using Bayesian Clustered Tensor Factorization
I. Sutskever, R. Salakhutdinov, J. Tenenbaum
[
pdf]
Learning to Hash with Binary Reconstructive Embeddings
B. Kulis, T. Darrell
[
pdf]
[
slide]
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More topics can be found at
ICML 2009
and
NIPS 2009