【文献阅读】Augmenting Supervised Neural Networks with Unsupervised Objectives-ICML-2016
一、Abstract 从近期对unsupervised learning 的研究得到启发，在large-scale setting 上，本文把unsupervised learning 与supervised learning结合起来，提高了supervised learning的性能。主要是把autoencoder与CNN结合起来 二、Key words: SAE;SWWAE; reconstr
相关文章
- 1. 文献阅读 - Binarized Neural Networks
- 2. 读文献——《ImageNet classification with deep convolutional neural networks》
- 3. 【文献阅读】Non-local Neural Networks
- 4. 【文献阅读】Self-Normalizing Neural Networks
- 5. Recurrent neural networks for multivariate time series with missing values文献阅读
- 6. 文献阅读（44）
- 7. 论文阅读——Unsupervised Neural Machine Translation with Indirect Supervision
- 8. Weakly supervised object recognition with convolutional neural networks 论文解读
- 9. 《Collaborative Filtering with Recurrent Neural Networks》阅读
- 10. 文献阅读
- 更多相关文章...
- • RSS 阅读器 - RSS 教程
- • C# 文本文件的读写 - C#教程
- • JDK13 GA发布：5大特性解读
- • Scala 中文乱码解决
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 文献阅读 - Binarized Neural Networks
- 2. 读文献——《ImageNet classification with deep convolutional neural networks》
- 3. 【文献阅读】Non-local Neural Networks
- 4. 【文献阅读】Self-Normalizing Neural Networks
- 5. Recurrent neural networks for multivariate time series with missing values文献阅读
- 6. 文献阅读（44）
- 7. 论文阅读——Unsupervised Neural Machine Translation with Indirect Supervision
- 8. Weakly supervised object recognition with convolutional neural networks 论文解读
- 9. 《Collaborative Filtering with Recurrent Neural Networks》阅读
- 10. 文献阅读