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arXiv最受关注五篇深度学习论文及其解读/笔记(Hugo Larochelle)

2015-10-5 10:22| 发布者: admin| 查看: 8525| 评论: 0|原作者: 爱可可-爱生活|来自: 机器学习研究会

摘要: 1. Training recurrent networks online without backtracking Authors: Yann Ollivier, Guillaume Charpiat Date posted to arXiv: 28 Jul 20152. Semi-Supervised Learning with Ladder Network Authors: Antti Ra ...

1. Training recurrent networks online without backtracking

Authors: Yann Ollivier, Guillaume Charpiat

Date posted to arXiv: 28 Jul 2015

2. Semi-Supervised Learning with Ladder Network

Authors: Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, Tapani Raiko

Date posted to arXiv: 9 Jul 2015

3. Towards Neural Network-based Reasoning

Authors: Baolin Peng, Zhengdong Lu, Hang Li, Kam-Fai Wong

Date posted to arXiv: 22 Aug 2015

4. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Authors: Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer

Date posted to arXiv: 9 Jun 2015

5. LSTM: A Search Space Odyssey

Authors: Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber

Date posted to arXiv: 13 Mar 2015


链接:

http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html/2


原文链接:

http://m.weibo.cn/1402400261/3893394268499715?sourcetype=page&lfid=2302592000382195&lcardid=2302592000382195_-_3893394268499715&mid=3893394268499715&luicode=10000011&_status_id=3893394268499715&uicode=10000002


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