开课单位--圭尔夫大学
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An Overview of Deep Learning and Its Challenges for Technical Computing[深度学习及其对技术计算的挑战概述]
  Graham Taylor(圭尔夫大学) An Overview of Deep Learning and Its Challenges for Technical Computing
热度：22

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Dynamical Binary Latent Variable Models for 3D Human Pose Tracking[动态二进制潜变量模型的三维人体姿态跟踪]
  Graham Taylor(圭尔夫大学) We introduce a new probability like latent variable model, which is called the conditional constrained Boltzmann implicit hybrid (imcrbm) for human po...
热度：54

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Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style[用于运动风格建模的因子条件受限Boltzmann机]
  Graham Taylor(圭尔夫大学) The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits...
热度：74

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Learning multi-scale temporal dynamics with recurrent neural networks[用递归神经网络学习多尺度时间动力学]
  Graham Taylor(圭尔夫大学) The last three years have seen an explosion of activity studying recurrent neural networks (RNNs), a generalization of feedforward neural networks whi...
热度：50

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A tutorial on deep and unsupervised feature learning for activity recognition[关于活动识别的深度和无监督特征学习的教程]
  Graham Taylor(圭尔夫大学) Recognition of human activity from video data is a challenging problem that has received an increasing amount of attention from the computer vision co...
热度：94

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Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style[基于因子约束条件的玻尔兹曼机建模]
  Graham Taylor(圭尔夫大学) The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits...
热度：98
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