Shakhnarovich, Greg - Toyota Technological Institute at Chicago

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Shakhnarovich, Greg - Toyota Technological Institute at Chicago

STATISTICAL DATA CLONING FOR MACHINE LEARNING RESEARCH THESIS
Nonlinear physically-based models for decoding motor-cortical population activity
Performance of Approximate Nearest Neighbor Classification Gregory Shakhnarovich John W. Fisher
Learning Image Patch Similarity The ability to compare image regions (patches) has been the basis of many approaches
Introduction The need to automatically decide whether, and/or to what extent, two objects are
This chapter presents some background for the research presented in this thesis. We start with a review, in Sections 2.1 and 2.2, of example-based classification and re-
Articulated Pose Estimation In this chapter we describe a new approach to estimation of articulated pose of hu-
Articulated Tracking This chapter describes two state-of-the-art probabilistic articulated tracking systems
Conclusions In this concluding chapter we summarize the contributions of this thesis and the
Bibliography [1] Delve Datasets. http://www.cs.toronto.edu/ delve/data/datasets.html.
Boosted Dyadic Kernel Discriminants Baback Moghaddam
A Slicing-Based Coherence Measure for Clusters of DTI Integral Curves
Learning Task-Specific Similarity Gregory Shakhnarovich
Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation
A Unified Learning Framework for Real Time Face Detection and Classification
Learning embeddings that reflect This chapter describes a family of algorithms for learning an embedding
Sparse Coding for Learning Interpretable Spatio-Temporal Primitives
Viewpoint-Aware Object Detection and Pose Estimation Daniel Glasner1
Sparse Coding for Learning Interpretable Spatio-Temporal Primitives
Similarity Sensitive Nonlinear Embeddings Dhruv Batra Gregory Shakhnarovich