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We study random embeddings produced by untrained neural set functions, and show that they are powerful representations which well capture the input features for downstream tasks such as classification, and are often linearly separable.
No training required: Exploring random encoders for sentence classification
John Wieting and Douwe Kiela · 1901
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Weight agnostic neural networks
Adam Gaier and David Ha · 1906
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Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition
T. M. Cover · 1965
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Visualizing data using t-sne
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Xavier Glorot and Yoshua Bengio · 2010
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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A powerful generative model using random weights for the deep image representation
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky · 2017
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Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J. Smola · 2017
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Learning representations and generative models for 3D point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
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Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky · 2016
Cited alongside, same era.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T Freeman, and Joshua B Tenenbaum · 2016
Cited alongside, same era.
On the limitations of representing functions on sets
Edward Wagstaff, Fabian B Fuchs, Martin Engelcke, Ingmar Posner, and Michael Osborne · 2019
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