Ce Liu, Jenny Yuen, and Antonio Torralba. Nonparametric Scene Parsing via Label Transfer, PAMI 2011.
Nitish Srivastava and Ruslan Salakhutdinov. Multimodal Learning with Deep Boltzmann Machines.
Neural Information Processing Systems (NIPS 26), 2012.
Richard Roberts, Sudipta N. Sinha, Richard Szeliski, Drew Steedly.
Structure from motion for scenes with large duplicate structures. CVPR 2011.
Week 4 (03/21/2013): Moo K. Chung, Associate Professor, Biostatistics and Medical Informatics.
Title: Exploiting hidden persistent homological structures in compressed sensing and sparse likelihood and its application to graphs and networks.
Abstract: We will explicitly show how to identify persistent homology in compressed sensing and sparse likelihood problems related to graphs/networks and completely bypass time consuming numerical optimization.
The following paper will be discussed in detail:http://www.stat.wisc.edu/~mchung/papers/chung.2012.NIPS.pdf
Week 5 (04/04/2013): Jerry Zhu, Associate Professor, Computer Sciences.
Title: Introduction to Persistent Homology
Time: April 4, 3:30pm-4:30pm, CS4310 ( note we will be half an hour earlier )
Abstract: Persistent homology is a rapidly growing branch of topology, gaining increasing interest in the machine learning community. The 0-th order homology groups correspond to clusters, which are the bread-and-butter of modern data analysis. The 1st order homology groups are "holes," as in the center of a donut; The 2nd order homology groups are "voids," as the inside of a balloon; and so on. These seemingly exotic higher-order mathematical structures may provide valuable invariant data representations that complement current feature-based representations. This talk will be a gentle introduction to persistent homology accessible to all computer scientists.
Week 6 (04/25/2013): Jie Liu
Title: Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies.
Time/place: April 25, 4pm-5pm, CS4310
Abstract: Large-scale multiple testing tasks often exhibit dependence, and
leveraging the dependence between individual tests is still one
challenging and important problem in statistics. With recent advances in
graphical models, it is feasible to use them to perform multiple testing
under dependence. We propose a multiple testing procedure which is based
on a Markov-random-field-coupled mixture model. The ground truth of
hypotheses is represented by a latent binary Markov random field, and the
observed test statistics appear as the coupled mixture variables. The
parameters in our model can be automatically learned by a novel EM
algorithm. We use an MCMC algorithm to infer the posterior probability
that each hypothesis is null, and the false discovery rate can be
controlled accordingly. Simulations show that the numerical performance of
multiple testing can be improved substantially by using our procedure. We
apply the procedure to a real-world genome-wide association study on
breast cancer, and we identify several SNPs with strong association
Week 7 (05/16/2013): Jia Xu
Title: Incorporating Topological Constraints within Interactive Segmentation and Contour Completion via Discrete Calculus.
Time/place: May 16, 4pm-5pm, CS4310
Abstract: We study the problem of interactive segmentation and contour completion for multiple objects. The form of constraints our model incorporates are those coming from user scribbles (interior or exterior constraints) as well as information regarding the topology of the 2-D space after partitioning (number of closed contours desired). We discuss how concepts from discrete calculus and a simple identity using the Euler characteristic of a planar graph can be utilized to derive a practical algorithm for this problem. We also present specialized branch and bound methods for the case of single contour completion under such constraints. On an extensive dataset of ~1000 images, our experiments suggest that a small amount of side knowledge can give strong improvements over fully unsupervised contour completion methods. We show that by interpreting user indications topologically, user effort is substantially reduced. More details can be found at http://pages.cs.wisc.edu/~jiaxu/projects/euler-seg/.