Fetching the paper…
Reading the bibliography…
Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.
An axiomatic basis for computer programming
Hoare, Charles Antony Richard · 1969
Earlier work this paper cites.
Generalization of back-propagation to recurrent neural networks
Pineda, Fernando J · 1987
Earlier work this paper cites.
Representing part-whole hierarchies in connectionist networks
Hinton, Geoffrey E · 1988
Earlier work this paper cites.
A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
Almeida, Luis B · 1990
Earlier work this paper cites.
Learning task-dependent distributed representations by backpropagation through structure
Goller, Christoph and Kuchler, Andreas · 1996
Earlier work this paper cites.
Supervised neural networks for the classification of structures
Sperduti, Alessandro and Starita, Antonina · 1997
Earlier work this paper cites.
Local reasoning about programs that alter data structures
O’Hearn, Peter, Reynolds, John C., and Yang, Hongseok · 2001
Earlier work this paper cites.
Separation logic: A logic for shared mutable data structures
Reynolds, John C · 2002
Earlier work this paper cites.
Marginalized kernels between labeled graphs
Kashima, Hisashi, Tsuda, Koji, and Inokuchi, Akihiro · 2003
Earlier work this paper cites.
Neural methods for non-standard data
Hammer, Barbara and Jain, Brijnesh J · 2004
Earlier work this paper cites.
A new model for learning in graph domains
Gori, Marco, Monfardini, Gabriele, and Scarselli, Franco · 2005
Earlier work this paper cites.
A comparison between recursive neural networks and graph neural networks
Di Massa, Vincenzo, Monfardini, Gabriele, Sarti, Lorenzo, Scarselli, Franco, Maggini, Marco, and Gori, Marco · 2006
Earlier work this paper cites.
Neural network for graphs: A contextual constructive approach
Micheli, Alessio · 2009
Cited alongside, same era.
The graph neural network model
Scarselli, Franco, Gori, Marco, Tsoi, Ah Chung, Hagenbuchner, Markus, and Monfardini, Gabriele · 2009
Cited alongside, same era.
Parameter learning with truncated message-passing
Domke, Justin · 2011
Cited alongside, same era.
Weisfeiler-lehman graph kernels
Shervashidze, Nino, Schweitzer, Pascal, Van Leeuwen, Erik Jan, Mehlhorn, Kurt, and Borgwardt, Karsten M · 2011
Cited alongside, same era.
Parsing natural scenes and natural language with recursive neural networks
Socher, Richard, Lin, Cliff C, Manning, Chris, and Ng, Andrew Y · 2011
Cited alongside, same era.
Empirical risk minimization of graphical model parameters given approximate inference, decoding, and model structure
Stoyanov, Veselin, Ropson, Alexander, and Eisner, Jason · 2011
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, Kyunghyun, Van Merriënboer, Bart, Gulcehre, Caglar, Bahdanau, Dzmitry, Bougares, Fethi, Schwenk, Holger, and Bengio, Yoshua · 2014
Later among the works it cites.
Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
Later among the works it cites.
Deepwalk: Online learning of social representations
Perozzi, Bryan, Al-Rfou, Rami, and Skiena, Steven · 2014
Later among the works it cites.
GRASShopper - complete heap verification with mixed specifications
Piskac, Ruzica, Wies, Thomas, and Zufferey, Damien · 2014
Later among the works it cites.
Learning to decipher the heap for program verification
Brockschmidt, Marc, Chen, Yuxin, Cook, Byron, Kohli, Pushmeet, and Tarlow, Daniel · 2015
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Neural networks for relational learning: an experimental comparison
Uwents, Werner, Monfardini, Gabriele, Blockeel, Hendrik, Gori, Marco, and Scarselli, Franco · 2011
Cited alongside, same era.
Spectral networks and locally connected networks on graphs
Bruna, Joan, Zaremba, Wojciech, Szlam, Arthur, and LeCun, Yann · 2013
Cited alongside, same era.
Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules
Lusci, Alessandro, Pollastri, Gianluca, and Baldi, Pierre · 2013
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Bahdanau, Dzmitry, Cho, Kyunghyun, and Bengio, Yoshua · 2014
Cited alongside, same era.
From machine learning to machine reasoning
Bottou, Léon · 2014
Cited alongside, same era.
Duvenaud, David, Maclaurin, Dougal, Aguilera-Iparraguirre, Jorge, Gómez-Bombarelli, Rafael, Hirzel, Timothy, Aspuru-Guzik, Alán, and Adams, Ryan P · 2015
Closest in time.
Ask me anything: Dynamic memory networks for natural language processing
Kumar, Ankit, Irsoy, Ozan, Su, Jonathan, Bradbury, James, English, Robert, Pierce, Brian, Ondruska, Peter, Gulrajani, Ishaan, and Socher, Richard · 2015
Closest in time.
Sukhbaatar, Sainbayar, Szlam, Arthur, Weston, Jason, and Fergus, Rob · 2015
Closest in time.
Improved semantic representations from tree-structured long short-term memory networks
Tai, Kai Sheng, Socher, Richard, and Manning, Christopher D · 2015
Closest in time.
Vinyals, Oriol, Fortunato, Meire, and Jaitly, Navdeep · 2015
Closest in time.
Towards ai-complete question answering: a set of prerequisite toy tasks
Weston, Jason, Bordes, Antoine, Chopra, Sumit, and Mikolov, Tomas · 2015
Closest in time.