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Graph Neural Networks for Learning Equivariant Representations of Neural Networks

18 March 2024

Boris Knyazev

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Papers citing "Graph Neural Networks for Learning Equivariant Representations of Neural Networks"

28 / 28 papers shown

Title
A Model Zoo of Vision Transformers Damian Falk Léo Meynent Florence Pfammatter Konstantin Schurholt Damian Borth 252 1 0 14 Apr 2025
Neural Solver Selection for Combinatorial Optimization Chengrui Gao Haopu Shang Ke Xue Chao Qian 82 0 0 13 Oct 2024
Monomial Matrix Group Equivariant Neural Functional Networks Hoang V. Tran Thieu N. Vo Tho H. Tran An T. Nguyen Tan M. Nguyen 131 9 0 18 Sep 2024
Grounding Continuous Representations in Geometry: Equivariant Neural Fields David R. Wessels David M. Knigge Samuele Papa Riccardo Valperga Sharvaree P. Vadgama E. Gavves Erik J. Bekkers 125 10 0 09 Jun 2024
Permutation Equivariant Neural Functionals Allan Zhou Kaien Yang Kaylee Burns Adriano Cardace Yiding Jiang Samuel Sokota J. Zico Kolter Chelsea Finn 103 56 0 27 Feb 2023
NeRN -- Learning Neural Representations for Neural Networks Maor Ashkenazi Zohar Rimon Ron Vainshtein Shir Levi Elad Richardson Pinchas Mintz Eran Treister 3DH 78 13 0 27 Dec 2022
VeLO: Training Versatile Learned Optimizers by Scaling Up Luke Metz James Harrison C. Freeman Amil Merchant Lucas Beyer ... Naman Agrawal Ben Poole Igor Mordatch Adam Roberts Jascha Narain Sohl-Dickstein 113 60 0 17 Nov 2022
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights Konstantin Schurholt Boris Knyazev Xavier Giró-i-Nieto Damian Borth 122 42 0 29 Sep 2022
Learning to Learn with Generative Models of Neural Network Checkpoints William S. Peebles Ilija Radosavovic Tim Brooks Alexei A. Efros Jitendra Malik UQCV 150 69 0 26 Sep 2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison Luke Metz Jascha Narain Sohl-Dickstein 87 21 0 22 Sep 2022
Practical tradeoffs between memory, compute, and performance in learned optimizers Luke Metz C. Freeman James Harrison Niru Maheswaranathan Jascha Narain Sohl-Dickstein 145 32 0 22 Mar 2022
Parameter Prediction for Unseen Deep Architectures Boris Knyazev M. Drozdzal Graham W. Taylor Adriana Romero Soriano OOD 100 83 0 25 Oct 2021
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 254 236 0 23 Mar 2021
Training Stronger Baselines for Learning to Optimize Tianlong Chen Weiyi Zhang Jingyang Zhou Shiyu Chang Sijia Liu Lisa Amini Zhangyang Wang OffRL 83 52 0 18 Oct 2020
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves Luke Metz Niru Maheswaranathan C. Freeman Ben Poole Jascha Narain Sohl-Dickstein 144 61 0 23 Sep 2020
Implicit Neural Representations with Periodic Activation Functions Vincent Sitzmann Julien N. P. Martel Alexander W. Bergman David B. Lindell Gordon Wetzstein AI4TS 174 2,579 0 17 Jun 2020
Principal Neighbourhood Aggregation for Graph Nets Gabriele Corso Luca Cavalleri Dominique Beaini Pietro Lio Petar Velickovic GNN 140 677 0 12 Apr 2020
Classifying the classifier: dissecting the weight space of neural networks Gabriel Eilertsen Daniel Jonsson Timo Ropinski Jonas Unger Anders Ynnerman 68 54 0 13 Feb 2020
Understanding and correcting pathologies in the training of learned optimizers Luke Metz Niru Maheswaranathan Jeremy Nixon C. Freeman Jascha Narain Sohl-Dickstein ODL 98 148 0 24 Oct 2018
FiLM: Visual Reasoning with a General Conditioning Layer Ethan Perez Florian Strub H. D. Vries Vincent Dumoulin Aaron Courville FAtt AIMat OffRL AI4CE 372 2,239 0 22 Sep 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 289 8,928 0 25 Aug 2017
Neural Message Passing for Quantum Chemistry Justin Gilmer S. Schoenholz Patrick F. Riley Oriol Vinyals George E. Dahl 598 7,500 0 04 Apr 2017
Learned Optimizers that Scale and Generalize Olga Wichrowska Niru Maheswaranathan Matthew W. Hoffman Sergio Gomez Colmenarejo Misha Denil Nando de Freitas Jascha Narain Sohl-Dickstein AI4CE 90 284 0 14 Mar 2017
Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf Max Welling GNN SSL 684 29,183 0 09 Sep 2016
Layer Normalization Jimmy Lei Ba J. Kiros Geoffrey E. Hinton 437 10,548 0 21 Jul 2016
Learning to learn by gradient descent by gradient descent Marcin Andrychowicz Misha Denil Sergio Gomez Colmenarejo Matthew W. Hoffman David Pfau Tom Schaul Brendan Shillingford Nando de Freitas 132 2,009 0 14 Jun 2016
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 469 43,357 0 11 Feb 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,575 0 04 Sep 2014