图 书 馆
可微的稀疏编码Differentiable Sparse Coding |
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| 课程网址: | http://videolectures.net/cmulls08_bradley_dsc/ |
| 主讲教师: | David Bradley |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Prior work has shown that features which appear to be biologically plausible as well as empirically useful can be found by sparse coding with a prior such as a Laplacian (L1 ) that promotes sparsity. We show how smoother priors can preserve the benefits of these sparse priors while adding stability to the Maximum A-Posteriori (MAP) estimate that makes it more useful for prediction problems. Additionally, we show how to calculate the derivative of the MAP estimate efficiently with implicit differentiation. One prior that can be differentiated this way is KL-regularization. We demonstrate its effectiveness on a wide variety of applications, and find that online optimization of the parameters of the KL-regularized model can significantly improve prediction performance. |
| 关 键 词: | 计算机科学; 机器学习; 稀疏编码 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-12：cwx |
| 阅读次数: | 34 |