图 书 馆
超越反向传播:不确定性传播Beyond Backpropagation: Uncertainty Propagation |
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| 课程网址: | http://videolectures.net/iclr2016_lawrence_beyond_backpropagation... |
| 主讲教师: | Neil D. Lawrence |
| 开课单位: | 视频讲座网 |
| 开课时间: | 2016-05-27 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Deep learning is founded on composable functions that are structured to capture regularities in data and can have their parameters optimized by backpropagation (differentiation via the chain rule). Their recent success is founded on the increased availability of data and computational power. However, they are not very data efficient. In low data regimes parameters are not well determined and severe overfitting can occur. The solution is to explicitly handle the indeterminacy by converting it to parameter uncertainty and propagating it through the model. Uncertainty propagation is more involved than backpropagation because it involves convolving the composite functions with probability distributions and integration is more challenging than differentiation. We will present one approach to fitting such models using Gaussian processes. The resulting models perform very well in both supervised and unsupervised learning on small data sets. The remaining challenge is to scale the algorithms to much larger data. |
| 关 键 词: | 深度学习; 捕获规律; 反向传播; 链式法则 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-06：chenxin01 |
| 最后编审: | 2023-03-06：chenxin01 |
| 阅读次数: | 22 |