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Št. zadetkov: 7
Video in druga učna gradiva
Oznake: computer science;computer vision;motion and tracking
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 learn ...
Leto: 2010 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computer science;computer vision;video analysis
Recognition of human activity from video data is a challenging problem that has received an increasing amount of attention from the computer vision community in recent years. Currently the best performing methods at this task are based on engineered descriptors with explicit local geometric cues and ...
Leto: 2011 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computer science;machine learning
The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits simple, exact inference. We present a new model, based on the CRBM that preserves its most important computational properties and includes multiplica ...
Leto: 2009 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computers;software;open-source software;programming;python;computer science;optimization methods;programming languages;machine learning;mathematics
Leto: 2014 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computer science;machine learning;deep learning;reinforcement learning;unsupervised learning
Leto: 2015 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computer science;machine learning;deep learning;reinforcement learning;unsupervised learning
Leto: 2015 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computer science;machine learning;physics;statistical physics
The last three years have seen an explosion of activity studying recurrent neural networks (RNNs), a generalization of feedforward neural networks which can map sequences to sequences. Training RNNs using backpropagation through time can be difficult, and was thought up until recently to be hopel ...
Leto: 2015 Vir: videolectures.net
Št. zadetkov: 7
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