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We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers of our model capture image information in a variety of forms: low-level edges, mid-level edge junctions, high-level object parts and complete objects. To build our model we rely on a novel inference scheme that ensures each...
We introduce a new class of probabilistic latent variable model called the Implicit Mixture of Conditional Restricted Boltzmann Machines (imCRBM) for use in human pose tracking. Key properties of the imCRBM are as follows: (1) learning is linear in the number of training exemplars so it can be learned from large datasets; (2) it learns coherent models of multiple activities; (3) it automatically discovers...
The initial assembly of Space Station Freedom involves the Space Shuttle and its remote manipulating system (RMS), modeling and analysis of which is made formidable by kinematic and joint nonlinearities. In this paper, continuum models are used instead of finite element models because of the improved accuracy, reduced number of system parameters, the avoidance of the model order reduction and the...
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