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- W2228667072 abstract "Deep Learning, a sub-area of machine learning, has become a buzz word in recent days due to itsgreat successes in many applications of machine learning, including speech processing, computervision and natural language processing. Deep learning became famous in the initial days throughthe successful application of Convolutional Neural Networks as well as Energy-based Models -orRestricted Boltzmann Machines (RBMs) - on handwritten digit recognition. While the last decadehas seen the growing use of convolution-based deep learning methods for image analysis, limitedwork has been done in adapting deep learning to video analysus. Existing methods have largelyextended the ideas based on convolution applied to images into the video analysis setting. Theprimary deep learning approaches that have been proposed so far explicitly for video sequences arethe 3D Convolutional Neural Networks and the Convolutional Gated RBM." @default.
- W2228667072 created "2016-06-24" @default.
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- W2228667072 date "2014-01-01" @default.
- W2228667072 modified "2023-09-26" @default.
- W2228667072 title "Temporal Coherence in Energy-based Deep Learning Machines for Action Recognition" @default.
- W2228667072 hasPublicationYear "2014" @default.
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