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基于因子约束条件的玻尔兹曼机建模

Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style
课程网址: http://videolectures.net/icml09_taylor_fcr/  
主讲教师: Graham Taylor
开课单位: 圭尔夫大学
开课时间: 2009-08-26
课程语种: 英语
中文简介:
条件限制玻尔兹曼机(CRBM)是最近提出的时间序列模型,具有丰富的分布式隐藏状态,并允许简单,精确的推理。我们提出了一种基于CRBM的新模型,该模型保留了其最重要的计算属性,并包括乘法三向交互,允许两个单元之间的有效交互权重由第三单元的动态状态调制。我们将乘法模型隐含的三向权重张量分解,将参数数量从O(N ^ 3)减少到O(N ^ 2)。结果是一个高效,紧凑的模型,我们通过对人体运动建模来证明其有效性。与CRBM一样,我们的模型可以使用一组参数捕获不同类型的运动,三向交互极大地提高了模型混合运动样式或在它们之间平滑过渡的能力。
课程简介: 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 multiplicative three-way interactions that allow the effective interaction weight between two units to be modulated by the dynamic state of a third unit. We factorize the three-way weight tensor implied by the multiplicative model, reducing the number of parameters from O(N^3) to O(N^2). The result is an efficient, compact model whose effectiveness we demonstrate by modeling human motion. Like the CRBM, our model can capture diverse styles of motion with a single set of parameters, and the three-way interactions greatly improve the model's ability to blend motion styles or to transition smoothly between them.
关 键 词: 时间序列模型; 乘法三向交互; 乘法模型
课程来源: 视频讲座网
最后编审: 2019-04-24:lxf
阅读次数: 108