Shakhnarovich g learning task specific similarity phd thesis mit 2006

Shakhnarovich g learning task specific similarity phd thesis mit 2006

Feb 04,  · Apr 04,  · Gregory Shakhnarovich. Learning Task-Specific Similarity. Thesis. Google Scholar; Gregory Shakhnarovich, Trevor Darrell, and Piotr Indyk. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. The MIT Press (). Google Scholar; Gregory Shakhnarovich, Paul Viola, and Trevor Darrell. PhD thesis, MIT, Learning Task-Specific Similarity. Advisor: Trevor Darrell. MsC thesis, Technion, Statistical Data Cloning for Machine Learning. Advisors: Ran El-Yaniv and Yoram Baram. Teaching I regularly teach two courses at TTIC/University of Chicago: TTIC , Introduction to Machine Learning (next offering by me: Autumn

Learning task-specific similarity

Since FebruaryI am an Assistant Professor at TTI-Chicagoshakhnarovich g learning task specific similarity phd thesis mit 2006, a philanthropically endowed academic computer science institute located on the University of Chicago campus. We at TTI-Chicago continue to admit students to our PhD program.

Please contact me for details. Prior to coming to TTI-Chicago, I was a post-doctoral researcher at the Department of Computer Science of Brown University where I worked with Michael Black. I received my PhD degree at MIT where I worked at CSAIL with Trevor Darrell on computer vision and machine learning.

My thesis topic was Learning Task-Specific Similarity. Before coming to MIT, I was a graduate student in the Computer Science Department of the TechnionIsrael Institute of Technology in Haifa, Israel, where I got my MSc thesis under the advisement of Ran El-Yaniv and Yoram Baram.

I got my undergraduate degree in Math and CS from Hebrew University in Jerusalem, Israel. MsC thesis, Technion, Statistical Data Cloning for Machine Learning. Advisors: Ran El-Yaniv and Yoram Baram. PhD thesis, MIT, Learning Task-Specific Similarity. Advisor: Trevor Darrell. Kilian WeinbergerBrian Kulis and Dhruv Batra and I organized a NIPS workshop "Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity". Workshop presentation: P.

Yadollahpour, D. Batra, G. Shakhnarovich, "M-Best Modes: Diverse M-Best Solutions in MRFs", Workshop on Discrete Optimization in Machine Learning, NIPS Workshop presentation: D. Glasner, M. Galun, S. Alpert, R. Basri, G. Shakhnarovich, "Viewpoint-Aware Object Detection and Pose Estimation", ICCV Kim, G. Shakhnarovich, R. Urtasun, "Sparse Coding for Learning Interpretable Spatio-Temporal Primitives", NIPS, Vargas-Irwin, G. Shakhnarovich, P. Yadollahpour, J. Mislow, M.

Black, J. Donoghue, "Decoding Complete Reach and Grasp Actions from Local Primary Motor Cortex Populations", The Journal of Neuroscience, Ritz, G. Shakhnarovich, A. Salomon, B. Raphael, "Discovery of Phosphorylation Motif Mixtures in Phosphoproteomics Data". Bioinformatics, Demiralp, G. Shakhnarovich, S. Zhang, D. Laidlaw, "Slicing-based shakhnarovich g learning task specific similarity phd thesis mit 2006 measure for refining clusters of 3D curves.

Artemiadis, G. Shakhnarovich, C. Vargas-Irwin, J. Donoghue, M. Black, "Decoding grasp aperture from motor-cortical population activity", IEEE Conf. on Neural Engineering, Kim, M. Black, "Nonlinear physically-based models for decoding motor-cortical population activity", NIPS Taycher, G. Shakhnarovich, D. Demirdjian, and T.

Darrell, "Conditional Random People: Tracking Humans with CRFs and Grid Filters". Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, Srebro, G. Roweis, "An Investigation of Computational and Informational Limits in Gaussian Mixture Clustering". ICML, Shakhnarovich, J. Fisher, "Performance of Approximate Nearest Neighbor Classification". Poster presented at Machine Learning Workshop at Snowbird,with preliminary results work in progress.

Shakhnarovich, T. Darrell, P. Indyk, editors, "Nearest-Neighbors methods in Learning and Vision: Theory and Practice". MIT Press, Demirdjian, L. Shakhnarovich, K. Grauman, T. Darrell, "Avoiding the Streetlight Effect: Tracking by Exploring Likelihood Modes", Proceedings of the International Conference on Computer Vision, Ren, G.

Hodgins, H. Pfister, P. Viola, "Learning Silhouette Features for Control of Human Motion", ACM Shakhnarovich g learning task specific similarity phd thesis mit 2006 on Graphics, Aranjelovic, G. Fisher, R. Cippola, T. Darrell, "Face Recognition with Image Sets Using Manifold Density Divergence", Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, pp. Shakhnarovich, B. Moghaddam, "Face Recognition in Subspaces", In Handbook of Face Recognition, S.

Li and A. Jain, Ed. Grauman, G. Darrell, "Virtual Visual Hulls: Example-Based 3D Shape Inference from a Single Silhouette", Proceedings of the 2nd Workshop on Statistical Methods in Video Processing, Viola, T. Darrell, "Fast Pose Estimation with Parameter Sensitive Hashing", Proceedings of the International Conference on Computer Vision, Darrell, "Inferring 3D Structure with a Statistical Image-Based Shape Model", Proceedings of the International Conference on Computer Vision, Darrell, "A Bayesian Approach to Image-Based Visuall Hull Reconstruction", Proceedings IEEE Conf.

on Computer Vision and Pattern Recognition, Moghaddam, G. Shakhnarovich, "Boosted Dyadic Kernel Discriminants", NIPS, Fisher, T. Darrell, "Face recognition from long-term observations", Proceedings of European Conference on Computer Vision, Darrell, "On Probabilistic Combination of Face and Gait Cues for Identification", Proceedings of the Int.

on Automatic Face and Gesture Recognition, Viola, B. Moghaddam, "A Unified Learning Framework for Real Time Face Detection and Classification", Proceedings of the Int. El-Yaniv, Y. Baram, "Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation", Proccedings of International Conference on Machine Learning,

Grade 9: Graphing Quadratic Functions and Analyzing the Effects on its Graph

, time: 30:19

Publications of the UW ML Research Group

Learning a Tree of Metrics with Disjoint Visual Features Sung Ju Hwang Kristen Grauman Fei Sha University of Texas University of Texas University of Southern California Austin, TX Austin, TX Los Angeles, CA sjhwang@blogger.com grauman@blogger.com feisha@blogger.com Abstract We introduce an approach to learn discriminative PhD thesis, MIT, Learning Task-Specific Similarity. Advisor: Trevor Darrell. Recent work and other news. Kilian Weinberger, Brian Kulis and Dhruv Batra and I organized a NIPS workshop "Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity" PhD thesis, MIT, Learning Task-Specific Similarity. Advisor: Trevor Darrell. MsC thesis, Technion, Statistical Data Cloning for Machine Learning. Advisors: Ran El-Yaniv and Yoram Baram. Teaching I regularly teach two courses at TTIC/University of Chicago: TTIC , Introduction to Machine Learning (next offering by me: Autumn