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实验室内部学术交流安排(一)

作者:DMIR    发布时间:2016-11-27    浏览量:600

页面分享链接:http://dmirlab.com/document/321


2016年11月28日 19:30

主讲人:许柏炎

主题:Deep Learning

内容:主要是对先前的报告内容进行总结和梳理,以及深度学习的相关应用如QA、机器翻译等。


2016年11月21日 19:30

主讲人:蔡晓凤

主题:Rnn And Sentiment Analysis

Main reading:

*1.Mikael Boden,et al. A guide to recurrent neural networks and backpropagation.2001

Link: http://140.116.249.155/file.php/49468/rn_dallas.pdf

Extend readings:

1.PreranaSinghal and PushpakBhattacharyya, Sentiment Analysis and DeepLearning: ASurvey.

Link:http://www.cfilt.iitb.ac.in/resources/surveys/sentiment-deeplearning-2016-prerna.pdf

2.Sepp Hochreiter, Jurgen Schmidhuber .LONG SHORT-TERM MEMORY.1997.

Link: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf


2016年11月14日 19:30

主讲人:李嘉豪

主题:Convolution Nerual Network

Main reading:

*1.Notes on Convolutional Neural Networks

Link: http://cogprints.org/5869/1/cnn_tutorial.pdf

Extend readings:

1.Generalization and Network Design Strategies

Link:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.479&rep=rep1&type=pdf

2.Convolutional Neural Networks Applied to House Numbers Digit Classification

Link: https://arxiv.org/pdf/1204.3968.pdf


2016年10月31日 19:30

主讲人:黄家明

主题:word2vec

Main reading:

*1.Mikolov, Tomas, et al. Distributed representations of words and phrases and their compositionality.Advances in neural information processing systems. 2013.

2.Mikolov, Tomas, et al. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).

Extend readings:

1. Le, Quoc V., and Tomas Mikolov. Distributed Representations of Sentences and Documents.ICML. Vol. 14. 2014

Link: http://www.jmlr.org/proceedings/papers/v32/le14.pdf

2. Perozzi, Bryan, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.

Link: https://arxiv.org/pdf/1403.6652.pdf


(Papers with * mark are must read.)


2016年10月24日19:30

主讲人:陈瑶

主题:Brief Introduction of Deep Learning 

Reading List:

1. http://www.nature.com/nature/journal/v521/n7553/pdf/nature14539.pdf

2. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf