图 书 馆
参数估计的新见解New insights on parameter estimation |
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| 课程网址: | http://videolectures.net/nipsworkshops2013_de_freitas_insights/ |
| 主讲教师: | Nando de Freitas |
| 开课单位: | 牛津大学 |
| 开课时间: | 2014-10-06 |
| 课程语种: | 英语 |
| 中文简介: |  我将讨论参数估计的两个新发展。首先，我将证明通过仅学习少量权重并使用非参数方法预测其余权重，无论正则化，架构，算法和数据集如何选择，都可以训练大多数深度学习方法。通常，这种方法可以只学习10％的权重而不会降低准确性。其次，我将介绍一种新的方法（LAP），用于稀疏连通性的无环无向概率图形模型中的参数估计。在实际感兴趣的几个领域中，例如，在量子退火计算机中使用的网格MRF和嵌合晶格，以前的统计有效估计量在模型大小上具有指数计算复杂性。在这些领域中，新方法将复杂度从指数级降低到线性级。 |
| 课程简介: | I will discuss two new developments in parameter estimation. First, I will show that it is possible to train most deep learning approaches - regardless of the choice of regularization, architecture, algorithms and datasets - by learning only a small number of the weights and predicting the rest with nonparametric methods. Often, this approach makes it possible to learn only 10% of the weights without a drop in accuracy. Second, I will introduce a new method (LAP) for parameter estimation in loopy undirected probabilistic graphical models of sparse connectivity. In several domains of practical interest - e.g., grid MRFs and chimera lattices used in quantum annealing computers - previous statistically efficient estimators had an exponential computational complexity in the size of the model. In these domains, the new approach reduces the complexity from exponential to linear. |
| 关 键 词: | 数据集; 指数计算 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-30：zyk |
| 最后编审: | 2020-11-30：zyk |
| 阅读次数: | 26 |