图 书 馆
神经网络的实际变分推理Practical Variational Inference for Neural Networks |
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| 课程网址: | http://videolectures.net/nips2011_graves_networks/ |
| 主讲教师: | Alex Graves |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches proposed so far have only been applicable to a few simple network architectures. This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length loss function) that can be applied to most neural networks. Along the way it revisits several common regularisers from a variational perspective. It also provides a simple pruning heuristic that can both drastically reduce the number of network weights and lead to improved generalisation. Experimental results are provided for a hierarchical multidimensional recurrent neural network applied to the TIMIT speech corpus. |
| 关 键 词: | 神经网络; 计算机科学; 机器学习; 贝叶斯学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-02：毛岱琦(课程编辑志愿者) |
| 阅读次数: | 70 |