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深度学习与乘法互动
Deep Learning with Multiplicative Interactions |
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| 课程网址: |
http://videolectures.net/nips09_hinton_dlmi/
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| 主讲教师: |
Geoffrey E. Hinton
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| 开课单位: |
多伦多大学
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| 开课时间: |
2010-01-20
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| 课程语种: |
英语
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| 中文简介: |
可以从未标记的数据中有效地学习深度网络。使用仅具有一层潜在变量的简单学习模块一次一个地学习表示层。一个模块的潜在变量的值形成用于训练下一个模块的数据。最常用的模块是受限制的玻尔兹曼机器或自动编码器，对隐藏的活动进行稀疏性惩罚。虽然深度网络对于诸如对象识别，信息检索和动作捕捉数据建模等任务非常成功，但是简单的学习模块没有乘法交互，这对于某些类型的数据非常有用。该演讲将展示如何将三阶能量函数分解以产生一个简单的学习模块，该模块保留受限制的玻尔兹曼机器的有利特性，例如非常简单的精确推理和基于成对统计的非常简单的学习规则。新模块包含可用于各种无监督学习任务的乘法交互。多伦多大学的研究人员一直在使用这种类型的模块从图像序列中提取定向能量，从图像序列中提取密集流场。新模块还可用于允许运动样式混合运动捕捉数据的自动回归模型。最后，新模块可用于将眼睛位置与特征向量组合，以允许具有可变分辨率视网膜的系统在多个注视中集成关于形状的信息。
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| 课程简介: |
Deep networks can be learned efficiently from unlabeled data. The layers of representation are learned one at a time using a simple learning module that has only one layer of latent variables. The values of the latent variables of one module form the data for training the next module. The most commonly used modules are Restricted Boltzmann Machines or autoencoders with a sparsity penalty on the hidden activities. Although deep networks have been quite successful for tasks such as object recognition, information retrieval, and modeling motion capture data, the simple learning modules do not have multiplicative interactions which are very useful for some types of data. The talk will show how a third-order energy function can be factorized to yield a simple learning module that retains advantageous properties of a Restricted Boltzmann Machine such as very simple exact inference and a very simple learning rule based on pair-wise statistics. The new module contains multiplicative interactions that are useful for a variety of unsupervised learning tasks. Researchers at the University of Toronto have been using this type of module to extract oriented energy from image patches and dense flow fields from image sequences. The new module can also be used to allow the style of a motion to blend auto regressive models of motion capture data. Finally, the new module can be used to combine an eye-position with a feature-vector to allow a system that has a variable resolution retina to integrate information about shape over many fixations.
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| 关 键 词: |
数据; 玻尔兹曼机器; 信息检索
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-09-06：lxf
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| 阅读次数: |
43
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