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Neural networks efficiently encode learned information within their parameters.
Handwritten digit recognition with a back-propagation network
Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, R. Howard, Wayne Hubbard, and Lawrence Jackel · 1989
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A framework for the cooperation of learning algorithms
Léon Bottou and Patrick Gallinari · 1990
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On the algebraic structure of feedforward network weight spaces
Robert Hecht-Nielsen · 1990
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Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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A mathematical model for feed-forward neural networks : theoretical description and parallel applications
Cedric Gegout, Bernard Girau, and Fabrice Rossi · 1995
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Locally constrained graph homomorphisms—structure, complexity, and applications
Jiří Fiala and Jan Kratochvíl · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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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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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Sharp minima can generalize for deep nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Equivariance through parameter-sharing
Siamak Ravanbakhsh, Jeff Schneider, and Barnabas Poczos · 2017
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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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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2019
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Exploring randomly wired neural networks for image recognition
Saining Xie, Alexander Kirillov, Ross Girshick, and Kaiming He · 2019
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D-vae: A variational autoencoder for directed acyclic graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, and Yixin Chen · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 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
Federated learning with heterogeneous architectures using graph hypernetworks
Or Litany, Haggai Maron, David Acuna, Jan Kautz, Gal Chechik, and Sanja Fidler · 2022
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Velo: Training versatile learned optimizers by scaling up
Luke Metz, James Harrison, C Daniel Freeman, Amil Merchant, Lucas Beyer, James Bradbury, Naman Agrawal, Ben Poole, Igor Mordatch, Adam Roberts, et al · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 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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Recipe for a general, powerful, scalable graph transformer
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Graph representation learning
William L Hamilton · 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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Graph structure of neural networks
Jiaxuan You, Jure Leskovec, Kaiming He, and Saining Xie · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups
Marc Finzi, Max Welling, and Andrew Gordon Wilson · 2021
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Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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Model zoos: A dataset of diverse populations of neural network models
Konstantin Schürholt, Diyar Taskiran, Boris Knyazev, Xavier Giró-i Nieto, and Damian Borth · 2022
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Git re-basin: Merging models modulo permutation symmetries
Samuel Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2023
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Set-based neural network encoding
Bruno Andreis, Soro Bedionita, and Sung Ju Hwang · 2023
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Spatial functa: Scaling functa to imagenet classification and generation
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Neural processing of tri-plane hybrid neural fields
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Relational attention: Generalizing transformers for graph-structured tasks
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Hyperdiffusion: Generating implicit neural fields with weight-space diffusion
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Ffcv: Accelerating training by removing data bottlenecks
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Sign and basis invariant networks for spectral graph representation learning
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Deep learning on implicit neural representations of shapes
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Graph inductive biases in transformers without message passing
Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K Dokania, Mark Coates, Philip Torr, and Ser-Nam Lim · 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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Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
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Neural networks are graphs! graph neural networks for equivariant processing of neural networks
David W. Zhang, Miltiadis Kofinas, Yan Zhang, Yunlu Chen, Gertjan J. Burghouts, and Cees G. M. Snoek · 2023
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