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Predicting Neural Network Accuracy from Weights

Predicting Neural Network Accuracy from Weights

26 February 2020
Thomas Unterthiner
Daniel Keysers
Sylvain Gelly
Olivier Bousquet
Ilya O. Tolstikhin
ArXivPDFHTML

Papers citing "Predicting Neural Network Accuracy from Weights"

26 / 26 papers shown
Title
A Model Zoo on Phase Transitions in Neural Networks
A Model Zoo on Phase Transitions in Neural Networks
Konstantin Schurholt
Léo Meynent
Yefan Zhou
Haiquan Lu
Yaoqing Yang
Damian Borth
68
0
0
25 Apr 2025
Adiabatic Fine-Tuning of Neural Quantum States Enables Detection of Phase Transitions in Weight Space
Adiabatic Fine-Tuning of Neural Quantum States Enables Detection of Phase Transitions in Weight Space
Vinicius Hernandes
Thomas Spriggs
Saqar Khaleefah
E. Greplova
49
0
0
21 Mar 2025
Adversarial Robustness in Parameter-Space Classifiers
Adversarial Robustness in Parameter-Space Classifiers
Tamir Shor
Ethan Fetaya
Chaim Baskin
A. Bronstein
AAML
OOD
171
0
0
27 Feb 2025
Monomial Matrix Group Equivariant Neural Functional Networks
Monomial Matrix Group Equivariant Neural Functional Networks
Hoang V. Tran
Thieu N. Vo
Tho H. Tran
An T. Nguyen
Tan M. Nguyen
54
5
0
18 Sep 2024
Towards Scalable and Versatile Weight Space Learning
Towards Scalable and Versatile Weight Space Learning
Konstantin Schurholt
Michael W. Mahoney
Damian Borth
50
15
0
14 Jun 2024
Unleash Graph Neural Networks from Heavy Tuning
Unleash Graph Neural Networks from Heavy Tuning
Lequan Lin
Dai Shi
Andi Han
Zhiyong Wang
Junbin Gao
AI4CE
27
2
0
21 May 2024
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
Renchunzi Xie
Ambroise Odonnat
Vasilii Feofanov
I. Redko
Jianfeng Zhang
Bo An
UQCV
77
1
0
17 Jan 2024
Graph Metanetworks for Processing Diverse Neural Architectures
Graph Metanetworks for Processing Diverse Neural Architectures
Derek Lim
Haggai Maron
Marc T. Law
Jonathan Lorraine
James Lucas
AI4CE
34
31
0
07 Dec 2023
Equivariant Deep Weight Space Alignment
Equivariant Deep Weight Space Alignment
Aviv Navon
Aviv Shamsian
Ethan Fetaya
Gal Chechik
Nadav Dym
Haggai Maron
29
21
0
20 Oct 2023
Sparsified Model Zoo Twins: Investigating Populations of Sparsified
  Neural Network Models
Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models
D. Honegger
Konstantin Schurholt
Damian Borth
31
4
0
26 Apr 2023
Deep Learning on Implicit Neural Representations of Shapes
Deep Learning on Implicit Neural Representations of Shapes
Luca de Luigi
Adriano Cardace
Riccardo Spezialetti
Pierluigi Zama Ramirez
Samuele Salti
Luigi Di Stefano
29
46
0
10 Feb 2023
Equivariant Architectures for Learning in Deep Weight Spaces
Equivariant Architectures for Learning in Deep Weight Spaces
Aviv Navon
Aviv Shamsian
Idan Achituve
Ethan Fetaya
Gal Chechik
Haggai Maron
47
63
0
30 Jan 2023
Privacy and Efficiency of Communications in Federated Split Learning
Privacy and Efficiency of Communications in Federated Split Learning
Zongshun Zhang
Andrea Pinto
Valeria Turina
Flavio Esposito
I. Matta
FedML
38
32
0
04 Jan 2023
On the Relationship Between Explanation and Prediction: A Causal View
On the Relationship Between Explanation and Prediction: A Causal View
Amir-Hossein Karimi
Krikamol Muandet
Simon Kornblith
Bernhard Schölkopf
Been Kim
FAtt
CML
29
14
0
13 Dec 2022
Multi-Object Navigation with dynamically learned neural implicit
  representations
Multi-Object Navigation with dynamically learned neural implicit representations
Pierre Marza
L. Matignon
Olivier Simonin
Christian Wolf
32
23
0
11 Oct 2022
On the Strong Correlation Between Model Invariance and Generalization
On the Strong Correlation Between Model Invariance and Generalization
Weijian Deng
Stephen Gould
Liang Zheng
OOD
32
16
0
14 Jul 2022
CONTINUER: Maintaining Distributed DNN Services During Edge Failures
CONTINUER: Maintaining Distributed DNN Services During Edge Failures
A. Majeed
Peter Kilpatrick
I. Spence
Blesson Varghese
14
0
0
25 Apr 2022
Leveraging the Graph Structure of Neural Network Training Dynamics
Leveraging the Graph Structure of Neural Network Training Dynamics
Fatemeh Vahedian
Ruiyu Li
Puja Trivedi
Di Jin
Danai Koutra
AI4CE
GNN
21
3
0
09 Nov 2021
Hyper-Representations: Self-Supervised Representation Learning on Neural
  Network Weights for Model Characteristic Prediction
Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Konstantin Schurholt
Dimche Kostadinov
Damian Borth
SSL
23
14
0
28 Oct 2021
In Search of Probeable Generalization Measures
In Search of Probeable Generalization Measures
Jonathan Jaegerman
Khalil Damouni
M. M. Ankaralı
Konstantinos N. Plataniotis
19
2
0
23 Oct 2021
A Theory of PAC Learnability of Partial Concept Classes
A Theory of PAC Learnability of Partial Concept Classes
N. Alon
Steve Hanneke
R. Holzman
Shay Moran
14
50
0
18 Jul 2021
Deep Learning Through the Lens of Example Difficulty
Deep Learning Through the Lens of Example Difficulty
R. Baldock
Hartmut Maennel
Behnam Neyshabur
47
156
0
17 Jun 2021
What Does Rotation Prediction Tell Us about Classifier Accuracy under
  Varying Testing Environments?
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?
Weijian Deng
Stephen Gould
Liang Zheng
39
62
0
10 Jun 2021
An Investigation of the Weight Space to Monitor the Training Progress of
  Neural Networks
An Investigation of the Weight Space to Monitor the Training Progress of Neural Networks
Konstantin Schurholt
Damian Borth
24
3
0
18 Jun 2020
Predicting trends in the quality of state-of-the-art neural networks
  without access to training or testing data
Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
Charles H. Martin
Tongsu Peng
Peng
Michael W. Mahoney
36
101
0
17 Feb 2020
Norm-Based Capacity Control in Neural Networks
Norm-Based Capacity Control in Neural Networks
Behnam Neyshabur
Ryota Tomioka
Nathan Srebro
119
577
0
27 Feb 2015
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