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We propose a neural network weight encoding method for network property prediction that utilizes set-to-set and set-to-vector functions to efficiently encode neural network parameters.
A new measure of rank correlation
Maurice G Kendall · 1938
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On the algebraic structure of feedforward network weight spaces
Robert Hecht-Nielsen · 1990
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Visualizing data using t-sne
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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The street view house numbers (svhn) dataset
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and A Ng · 2018
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Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 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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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Regularizing towards permutation invariance in recurrent models
Edo Cohen-Karlik, Avichai Ben David, and Amir Globerson · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
Deep learning on implicit neural representations of shapes
Luca De Luigi, Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, and Luigi Di Stefano · 2023
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Regularizing towards soft equivariance under mixed symmetries
Hyunsu Kim, Hyungi Lee, Hongseok Yang, and Juho Lee · 2023
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Graph metanetworks for processing diverse neural architectures
Derek Lim, Haggai Maron, Marc T Law, Jonathan Lorraine, and James Lucas · 2023
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On genuine invariance learning without weight-tying
Artem Moskalev, Anna Sepliarskaia, Erik J Bekkers, and Arnold WM Smeulders · 2023
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Equivariant architectures for learning in deep weight spaces
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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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Mini-batch consistent slot set encoder for scalable set encoding
Andreis Bruno, Jeffrey Willette, Juho Lee, and Sung Ju Hwang · 2021
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Setvae: Learning hierarchical composition for generative modeling of set-structured data
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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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Unsupervised learning of equivariant structure from sequences
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Model zoos: A dataset of diverse populations of neural network models
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Permutation equivariant neural functionals
Allan Zhou, Kaien Yang, Kaylee Burns, Yiding Jiang, Samuel Sokota, J Zico Kolter, and Chelsea Finn
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Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, and Haggai Maron · 2023
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A unified framework to enforce, discover, and promote symmetry in machine learning
Samuel E Otto, Nicholas Zolman, J Nathan Kutz, and Steven L Brunton · 2023
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Scalable set encoding with universal mini-batch consistency and unbiased full set gradient approximation, 2023
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Graph neural networks for learning equivariant representations of neural networks
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Diffusion-based neural network weights generation
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