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Fast and Memory Efficient Linear Transformation with Structured Matrices - Circulant Matrices - Kronecker Factored Matrices - Hadamard-Diagonal Matrices - Applications in deep learning, binary embedding, kernel approximation, product quantization |
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Learning with Label Proportions (LLP) - Can we predict the individual labels given only some label statistics on groups? - Broad applications (and privacy concerns) in political science, marketing, healthcare etc. - Theoretical analysis to understand when and why LLP is possible. - Solving challenging problems in computer vision. |
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Attribute-based Visual Recognition - Framework and methods for designing discriminative visual attributes for recognition - Using attributes for large-scale image retrieval |