Fetching the paper…
Reading the bibliography…
Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks.
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
Yair Weiss and William T Freeman · 2001
Earlier work this paper cites.
Efficient belief propagation for early vision
Pedro F Felzenszwalb and Daniel P Huttenlocher · 2006
Earlier work this paper cites.
Fixing max-product: Convergent message passing algorithms for map lp-relaxations
Amir Globerson and Tommi S Jaakkola · 2008
Earlier work this paper cites.
Tensor decompositions and applications
Tamara G Kolda and Brett W Bader · 2009
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
Earlier work this paper cites.
Energy minimization for linear envelope mrfs
Pushmeet Kohli and M Pawan Kumar · 2010
Earlier work this paper cites.
libdai: A free and open source c++ library for discrete approximate inference in graphical models
Joris M Mooij · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
Cited alongside, same era.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
Cited alongside, same era.
AD3: Alternating directions dual decomposition for map inference in graphical models
André FT Martins, Mário AT Figueiredo, Pedro MQ Aguiar, Noah A Smith, and Eric P Xing · 2015
Cited alongside, same era.
3d semantic parsing of large-scale indoor spaces
Iro Armeni, Ozan Sener, Amir R Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese · 2016
Cited alongside, same era.
Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
Cited alongside, same era.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Later among the works it cites.
Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
Later among the works it cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Later among the works it cites.
Quantum-chemical insights from deep tensor neural networks
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, and Alexandre Tkatchenko · 2017
Later among the works it cites.
Tensor belief propagation
Andrew Wrigley, Wee Sun Lee, and Nan Ye · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Molecular graph convolutions: moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
A scalable active framework for region annotation in 3d shape collections
Li Yi, Vladimir G Kim, Duygu Ceylan, I Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, Leonidas Guibas, et al · 2016
Cited alongside, same era.
Later among the works it cites.
Neural network-based graph embedding for cross-platform binary code similarity detection
Xiaojun Xu, Chang Liu, Qian Feng, Heng Yin, Le Song, and Dawn Song · 2017
Later among the works it cites.
PointCNN: Convolution on
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
Later among the works it cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Later among the works it cites.
Dynamic Graph CNN for Learning on Point Clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon · 2019
Closest in time.