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Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals.
Approximation by superpositions of a sigmoidal function
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On a kernel-based method for pattern recognition, regression, approximation, and operator inversion
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A wavelet tour of signal processing
Stéphane Mallat · 1999
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Discrete-time signal processing
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L. Bartlett and Shahar Mendelson · 2001
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Random features for large-scale kernel machines
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Dictionary learning
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Fundamentals of Communication Systems
John G. Proakis and Masoud Salehi · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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The shattered gradients problem: If resnets are the answer, then what is the question?
David Balduzzi, Marcus Frean, Lennox Leary, JP Lewis, Kurt Wan-Duo Ma, and Brian McWilliams · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clement Hongler · 2018
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 2019
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Hypernetwork functional image representation
Sylwester Klocek, Łukasz Maziarka, Maciej Wołczyk, Jacek Tabor, Jakub Nowak, and Marek Śmieja · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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Texture fields: Learning texture representations in function space
Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
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Learning a neural 3d texture space from 2d exemplars
Philipp Henzler, Niloy J Mitra, and Tobias Ritschel · 2020
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Neural spectrum alignment: Empirical study
Dmitry Kopitkov and Vadim Indelman · 2020
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Neural spectrum alignment: Empirical study
Generative models as distributions of functions
Emilien Dupont, Yee Whye Teh, and Arnaud Doucet · 2021
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Multiplicative filter networks
Rizal Fathony, Anit Kumar Sahu, Devin Willmott, and J Zico Kolter · 2021
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Fastnerf: High-fidelity neural rendering at 200fps
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Baking neural radiance fields for real-time view synthesis
Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec · 2021
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Learning continuous representation of audio for arbitrary scale super resolution
Jaechang Kim, Yunjoo Lee, Seunghoon Hong, and Jungseul Ok · 2021
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Dmitry Kopitkov and Vadim Indelman · 2020
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Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
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Neural tangents: Fast and easy infinite neural networks in python
Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, and Samuel S. Schoenholz · 2020
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Metasdf: Meta-learning signed distance functions
Vincent Sitzmann, Eric R Chan, Richard Tucker, Noah Snavely, and Gordon Wetzstein · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Nerf in the wild: Neural radiance fields for unconstrained photo collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, and Daniel Duckworth · 2021
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Ringing relus: Harmonic distortion analysis of nonlinear feedforward networks
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Modulated periodic activations for generalizable local functional representations
Ishit Mehta, Michaël Gharbi, Connelly Barnes, Eli Shechtman, Ravi Ramamoorthi, and Manmohan Chandraker · 2021
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DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks
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Giraffe: Representing scenes as compositional generative neural feature fields
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What can linearized neural networks actually say about generalization?
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Geometric compression of invariant manifolds in neural networks
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Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields
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Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps, 2021
Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger · 2021
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Learned initializations for optimizing coordinate-based neural representations
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Advances in neural rendering
A. Tewari, O. Fried, J. Thies, V. Sitzmann, S. Lombardi, Z. Xu, T. Simon, M. Nießner, E. Tretschk, L. Liu, B. Mildenhall, P. Srinivasan, R. Pandey, S. Orts-Escolano, S. Fanello, M. Guo, G. Wetzstein, J.-Y. Zhu, C. Theobalt, M. Agrawala, D. B Goldman, and M. Zollhöfer · 2021
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Rethinking positional encoding
Jianqiao Zheng, Sameera Ramasinghe, and Simon Lucey · 2021
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