Fetching the paper…
Reading the bibliography…
We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text.
The estimation of the gradient of a density function, with applications in pattern recognition
Keinosuke Fukunaga and Larry D. Hostetler. 1975 · 1975
Earlier work this paper cites.
Mean shift, mode seeking, and clustering
Yizong Cheng. 1995 · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Leon Bottou, Y Bengio, and Patrick Haffner. 1998 · 1998
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
Earlier work this paper cites.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel P. Kuksa. 2011 · 2011
Earlier work this paper cites.
Hidden factors and hidden topics: understanding rating dimensions with review text
Julian J. McAuley and Jure Leskovec. 2013 · 2013
Earlier work this paper cites.
A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014 · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus. 2014 · 2014
Cited alongside, same era.
Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan L. Boyd-Graber, and Hal Daumé III. 2015 · 2015
Cited alongside, same era.
Effective use of word order for text categorization with convolutional neural networks
Rie Johnson and Tong Zhang. 2015 · 2015
Cited alongside, same era.
Deep learning
Yann LeCun, Y Bengio, and Geoffrey Hinton. 2015 · 2015
Cited alongside, same era.
Image-based recommendations on styles and substitutes
Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015 · 2015
Cited alongside, same era.
A primer on neural network models for natural language processing
Yoav Goldberg. 2016 · 2016
Later among the works it cites.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi S. Jaakkola. 2016 · 2016
Later among the works it cites.
”why should I trust you?”: Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Later among the works it cites.
A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi S. Jaakkola. 2017 · 2017
Later among the works it cites.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018 · 2018
Closest in time.
Neural network interpretation via fine grained textual summarization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Semantic clustering and convolutional neural network for short text categorization
Peng Wang, Jiaming Xu, Bo Xu, Cheng-Lin Liu, Heng Zhang, Fangyuan Wang, and Hongwei Hao. 2015 · 2015
Cited alongside, same era.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
Cited alongside, same era.
Pei Guo, Connor Anderson, Kolten Pearson, and Ryan Farrell. 2018 · 2018
Closest in time.
Learned deformation stability in convolutional neural networks
Avraham Ruderman, Neil C. Rabinowitz, Ari S. Morcos, and Daniel Zoran. 2018 · 2018
Closest in time.