Fetching the paper…
Reading the bibliography…
This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets).
Some methods of speeding up the convergence of iteration methods
Polyak, B.T · 1964
Earlier work this paper cites.
The Written Language Bias in Linguistics
Linell, P · 1982
Earlier work this paper cites.
Learning representations by back-propagating errors
Rumelhart, D.E., Hintont, G.E., and Williams, R.J · 1986
Earlier work this paper cites.
Inference in text understanding
Norvig, Peter · 1987
Earlier work this paper cites.
Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Building a machine learning based text understanding system
Soderland, Stephen · 2001
Earlier work this paper cites.
Identifying Collocations for Recognizing Opinions
Wiebe, Janyce M., Wilson, Theresa, and Bell, Matthew · 2001
Earlier work this paper cites.
Determining the sentiment of opinions
Kim, Soo-Min and Hovy, Eduard · 2004
Earlier work this paper cites.
Wordnet and wordnets
Fellbaum, Christiane · 2005
Earlier work this paper cites.
Automatic identification of sentiment vocabulary: exploiting low association with known sentiment terms
Gamon, Michael and Aue, Anthony · 2005
Earlier work this paper cites.
Understanding interobserver agreement: the kappa statistic
Viera, Anthony J, Garrett, Joanne M, et al · 2005
Earlier work this paper cites.
Recognizing contextual polarity in phrase-level sentiment analysis
Wilson, Theresa, Wiebe, Janyce, and Hoffmann, Paul · 2005
Earlier work this paper cites.
Learning to identify emotions in text
Strapparava, Carlo and Mihalcea, Rada · 2008
Cited alongside, same era.
Automatic online news issue construction in web environment
Wang, Canhui, Zhang, Min, Ma, Shaoping, and Ru, Liyun · 2008
Cited alongside, same era.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Cited alongside, same era.
Learning mid-level features for recognition
Boureau, Y-L, Bach, Francis, LeCun, Yann, and Ponce, Jean · 2010
Cited alongside, same era.
Rectified linear units improve restricted boltzmann machines
Nair, Vinod and Hinton, Geoffrey E · 2010
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, Geoffrey E, Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan R · 2012
Deep convolutional neural networks for sentiment analysis of short texts
dos Santos, Cicero and Gatti, Maira · 2014
Later among the works it cites.
DeepSpeech: Scaling up end-to-end speech recognition
Hannun, A., Case, C., Casper, J., Catanzaro, B., Diamos, G., Elsen, E., Prenger, R., Satheesh, S., Sengupta, S., Coates, A., and Ng, A. Y · 2014
Later among the works it cites.
Effective use of word order for text categorization with convolutional neural networks
Johnson, Rie and Zhang, Tong · 2014
Later among the works it cites.
Deep fragment embeddings for bidirectional image sentence mapping
Karpathy, Andrej, Joulin, Armand, and Fei-Fei, Li · 2014
Later among the works it cites.
Convolutional neural networks for sentence classification
Kim, Yoon · 2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Cited alongside, same era.
Devise: A deep visual-semantic embedding model
Frome, Andrea, Corrado, Greg S, Shlens, Jon, Bengio, Samy, Dean, Jeff, Mikolov, Tomas, et al · 2013
Cited alongside, same era.
Learning semantic representations for the phrase translation model
Gao, Jianfeng, He, Xiaodong, Yih, Wen-tau, and Deng, Li · 2013
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, Ross B., Donahue, Jeff, Darrell, Trevor, and Malik, Jitendra · 2013
Cited alongside, same era.
Hidden factors and hidden topics: Understanding rating dimensions with review text
McAuley, Julian and Leskovec, Jure · 2013
Cited alongside, same era.
Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, Pierre, Eigen, David, Zhang, Xiang, Mathieu, Michaël, Fergus, Rob, and LeCun, Yann · 2013
Cited alongside, same era.
Le, Quoc V and Mikolov, Tomas · 2014
Later among the works it cites.
DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia
Lehmann, Jens, Isele, Robert, Jakob, Max, Jentzsch, Anja, Kontokostas, Dimitris, Mendes, Pablo N., Hellmann, Sebastian, Morsey, Mohamed, van Kleef, Patrick, Auer, Sören, and Bizer, Christian · 2014
Later among the works it cites.
Glove: Global vectors for word representation
Pennington, Jeffrey, Socher, Richard, and Manning, Christopher D · 2014
Later among the works it cites.
CNN features off-the-shelf: an astounding baseline for recognition
Razavian, Ali Sharif, Azizpour, Hossein, Sullivan, Josephine, and Carlsson, Stefan · 2014
Later among the works it cites.
Show and tell: A neural image caption generator
Vinyals, Oriol, Toshev, Alexander, Bengio, Samy, and Erhan, Dumitru · 2014
Later among the works it cites.
Zaremba, Wojciech and Sutskever, Ilya · 2014
Later among the works it cites.
Visualizing and understanding convolutional networks
Zeiler, Matthew D and Fergus, Rob · 2014
Later among the works it cites.