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With the emergence of powerful representations of continuous data in the form of neural fields, there is a need for discretization invariant learning: an approach for learning maps between functions on continuous domains without being sensitive to how the function is sampled.
Chiyu "Max" Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, and Matthias Nießner · 1901
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove · 1901
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Generalizing discrete convolutions for unstructured point clouds
Alexandre Boulch · 1904
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 1905
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 1906
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Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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Tianping Chen and Hong Chen · 1993
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Tianping Chen, Hong Chen, and Ruey wen Liu · 1995
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Monte carlo and quasi-monte carlo methods
Russel E Caflisch · 1998
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Vincent Sitzmann, Eric R. Chan, Richard Tucker, Noah Snavely, and Gordon Wetzstein · 2006
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Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein · 2006
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Fourier features let networks learn high frequency functions in low dimensional domains
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Fourier neural operator for parametric partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2010
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Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, and Ren Ng · 2012
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Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas · 2017
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