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In many machine learning problems the output should not depend on the order of the input.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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The space complexity of approximating the frequency moments
Noga Alon, Yossi Matias, and Mario Szegedy · 1999
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An empirical evaluation of deep architectures on problems with many factors of variation
Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra, and Yoshua Bengio · 2007
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
2 Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
Earlier work this paper cites.
Compete to compute
Rupesh K Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, and Jürgen Schmidhuber · 2013
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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On the number of linear regions of deep neural networks
Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
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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
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2016
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Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
Ryan L Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2018
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Deep models of interactions across sets
Jason Hartford, Devon Graham, Kevin Leyton-Brown, and Siamak Ravanbakhsh · 2018
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Neural motifs: Scene graph parsing with global context
Rowan Zellers, Mzaark Yatskar, Sam Thomson, and Yejin Choi · 2018
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Mapping images to scene graphs with permutation-invariant structured prediction
Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, and Amir Globerson · 2018
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Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
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Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Invariant and equivariant graph networks
Haggai Maron, Heli Ben Hamu, Nadav Shamir, and Yaron Lipman · 2019
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Mnist-c: A robustness benchmark for computer vision
Norman Mu and Justin Gilmer · 2019
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