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Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors.
On the algebraic structure of feedforward network weight spaces
Robert Hecht-Nielsen · 1990
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Deep Residual Learning for Image Recognition
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Neural Message Passing for Quantum Chemistry
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Attention Is All You Need
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Learned Optimizers that Scale and Generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando Freitas, and Jascha Sohl-Dickstein · 2017
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Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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FiLM: Visual Reasoning with a General Conditioning Layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
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Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, and Jascha Sohl-Dickstein · 2019
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GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Marc Brockschmidt · 2020
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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Parameter Prediction for Unseen Deep Architectures
Boris Knyazev, Michal Drozdzal, Graham W Taylor, and Adriana Romero Soriano · 2021
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Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Konstantin Schürholt, Dimche Kostadinov, and Damian Borth · 2021
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Tutorial on amortized optimization for learning to optimize over continuous domains
Brandon Amos · 2022
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Learning to Optimize: A Primer and A Benchmark
Tianlong Chen, Xiaohan Chen, Wuyang Chen, Zhangyang Wang, Howard Heaton, Jialin Liu, and Wotao Yin · 2022
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Tianlong Chen, Weiyi Zhang, Zhou Jingyang, Shiyu Chang, Sijia Liu, Lisa Amini, and Zhangyang Wang · 2020
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Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
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Classifying the classifier: dissecting the weight space of neural networks
Gabriel Eilertsen, Daniel Jönsson, Timo Ropinski, Jonas Unger, and Anders Ynnerman · 2020
Cited alongside, same era.
Luke Metz, Niru Maheswaranathan, C Daniel Freeman, Ben Poole, and Jascha Sohl-Dickstein · 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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Implicit Neural Representations with Periodic Activation Functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Predicting Neural Network Accuracy from Weights
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, and Ilya Tolstikhin · 2020
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From data to functa: Your data point is a function and you can treat it like one
Emilien Dupont, Hyunjik Kim, SM Eslami, Danilo Rezende, and Dan Rosenbaum · 2022
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A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
James Harrison, Luke Metz, and Jascha Sohl-Dickstein · 2022
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Learning to Learn with Generative Models of Neural Network Checkpoints
William Peebles, Ilija Radosavovic, Tim Brooks, Alexei A Efros, and Jitendra Malik · 2022
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NeRN – Learning Neural Representations for Neural Networks
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Deep Learning on Implicit Neural Representations of Shapes
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Relational Attention: Generalizing Transformers for Graph-Structured Tasks
Cameron Diao and Ricky Loynd · 2023
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HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion
Ziya Erkoç, Fangchang Ma, Qi Shan, Matthias Nießner, and Angela Dai · 2023
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Equivariant Architectures for Learning in Deep Weight Spaces
Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, and Haggai Maron · 2023
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