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Learning to share visual appearance for multiclass object detection
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By R. Salakhutdinov,A. Torralba,J. Tenenbaum
Issue Date:June 2011
pp. 1481-1488
We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes ...
 
Robust Boltzmann Machines for recognition and denoising
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Yichuan Tang,R. Salakhutdinov,G. Hinton
Issue Date:June 2012
pp. 2264-2271
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this paper, we introduce a novel model, the Robust Boltzmann Machine (RoBM), which al...
 
Learning with Hierarchical-Deep Models
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By R. Salakhutdinov,J. B. Tenenbaum,A. Torralba
Issue Date:August 2013
pp. 1958-1971
We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP)...
 
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