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Learning representation for graph classification turns a variable-size graph into a fixed-size vector (or matrix).
Long short-term memory
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The columnar organization of the neocortex
Vernon B Mountcastle · 1997
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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A fast and simple algorithm for training neural probabilistic language models
Andriy Mnih and Yee Whye Teh · 2012
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Spectral networks and deep locally connected networks on graphs
Joan Bruna, W Zaremba, A Szlam, and Yann LeCun · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Distributed representations of sentences and documents
Quoc V Le and Tomas Mikolov · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Rupesh K Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Faster training of very deep networks via p-norm gates
Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh · 2016
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Predicting delivery capability in iterative software development
Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose, and John Grundy · 2017
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Highway and residual networks learn unrolled iterative estimation
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Column networks for collective classification
Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh · 2017
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Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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