Fetching the paper…
Reading the bibliography…
Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Weisfeiler-Lehman Graph Kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt · 2011
Earlier work this paper cites.
Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
Earlier work this paper cites.
Benchmark data sets for graph kernels, 2016
Kristian Kersting, Nils M. Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann · 2016
Earlier work this paper cites.
Propagation kernels: Efficient graph kernels from propagated information
Marion Neumann, Roman Garnett, Christian Bauckhage, and Kristian Kersting · 2016
Earlier work this paper cites.
Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
Earlier work this paper cites.
Rank-based pooling for deep convolutional neural networks
Zenglin Shi, Yangdong Ye, and Yunpeng Wu · 2016
Earlier work this paper cites.
End-to-end people detection in crowded scenes
Russell Stewart and Mykhaylo Andriluka · 2016
Earlier work this paper cites.
Parseval Networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Earlier work this paper cites.
A point set generation network for 3D object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J. Guibas · 2017
Cited alongside, same era.
Learning graphical state transitions
Daniel D. Johnson · 2017
Cited alongside, same era.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
Cited alongside, same era.
DeepSetNet: Predicting sets with deep neural networks
S. Hamid Rezatofighi., Vijay Kumar B G, Anton Milan, Ehsan Abbasnejad, Anthony Dick, and Ian Reid · 2017
Cited alongside, same era.
A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Tim Lillicrap · 2017
Cited alongside, same era.
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Gonzalo Mena, David Belanger, Scott Linderman, and Jasper Snoek · 2018
Later among the works it cites.
Learning relationship-aware visual features, 2018
Nicola Messina, Giuseppe Amato, Fabio Carrara, Fabrizio Falchi, and Claudio Gennaro · 2018
Later among the works it cites.
S. Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Daniel Cremers, Laura Leal-Taixé, and Ian Reid · 2018
Later among the works it cites.
Loss functions for multiset prediction
Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, and Kyunghyun Cho · 2018
Later among the works it cites.
FoldingNet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
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.
Learning representations and generative models for 3D point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas J Guibas · 2018
Cited alongside, same era.
Sorting out Lipschitz function approximation
Cem Anil, James Lucas, and Roger Grosse · 2018
Cited alongside, same era.
Towards sparse hierarchical graph classifiers
Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, and Pietro Liò · 2018
Cited alongside, same era.
SplineCNN: Fast geometric deep learning with continuous B-spline kernels
Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller · 2018
Cited alongside, same era.
Gather-Excite: Exploiting feature context in convolutional neural networks
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Andrea Vedaldi · 2018
Cited alongside, same era.
Compositional Attention Networks for Machine Reasoning
Drew A. Hudson and Christopher D. Manning · 2018
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
Later among the works it cites.
GraphRNN: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
Later among the works it cites.
An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
Later among the works it cites.
Graph U-Net, 2019
Hongyang Gao and Shuiwang Ji · 2019
Closest in time.
Stochastic optimization of sorting networks via continuous relaxations
Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon · 2019
Closest in time.
Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
Closest in time.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Closest in time.