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What can linear interpolation of neural network loss landscapes tell us?
v1v2 (latest)

What can linear interpolation of neural network loss landscapes tell us?

International Conference on Machine Learning (ICML), 2021
30 June 2021
Tiffany J. Vlaar
Jonathan Frankle
    MoMe
ArXiv (abs)PDFHTMLGithub

Papers citing "What can linear interpolation of neural network loss landscapes tell us?"

23 / 23 papers shown
A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
Minghui Chen
Hrad Ghoukasian
Ruinan Jin
Zehua Wang
Sai Praneeth Karimireddy
Xiaoxiao Li
206
0
0
16 Nov 2025
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions
Devin Kwok
Gül Sena Altıntaş
Colin Raffel
David Rolnick
475
3
0
16 Jun 2025
Connecting Independently Trained Modes via Layer-Wise Connectivity
Connecting Independently Trained Modes via Layer-Wise Connectivity
Yongding Tian
Zaid Al-Ars
Maksim Kitsak
P. Hofstee
3DPC
543
1
0
05 May 2025
High-dimensional manifold of solutions in neural networks: insights from statistical physics
High-dimensional manifold of solutions in neural networks: insights from statistical physics
Enrico M. Malatesta
378
5
0
20 Feb 2025
In Search of the Successful Interpolation: On the Role of Sharpness in
  CLIP Generalization
In Search of the Successful Interpolation: On the Role of Sharpness in CLIP Generalization
Alireza Abdollahpoorrostam
316
0
0
21 Oct 2024
Weight Scope Alignment: A Frustratingly Easy Method for Model Merging
Weight Scope Alignment: A Frustratingly Easy Method for Model MergingEuropean Conference on Artificial Intelligence (ECAI), 2024
Yichu Xu
Xin-Chun Li
Le Gan
De-Chuan Zhan
MoMe
369
3
0
22 Aug 2024
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
Derek Lim
Moe Putterman
Robin Walters
Haggai Maron
Stefanie Jegelka
592
19
0
30 May 2024
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
Xin-Chun Li
Jinli Tang
Bo Zhang
Lan Li
De-Chuan Zhan
399
2
0
21 May 2024
Visualizing, Rethinking, and Mining the Loss Landscape of Deep Neural Networks
Visualizing, Rethinking, and Mining the Loss Landscape of Deep Neural Networks
Yichu Xu
Xin-Chun Li
Lan Li
De-Chuan Zhan
438
3
0
21 May 2024
Simultaneous linear connectivity of neural networks modulo permutation
Simultaneous linear connectivity of neural networks modulo permutation
Ekansh Sharma
Devin Kwok
Tom Denton
Daniel M. Roy
David Rolnick
Gintare Karolina Dziugaite
500
9
0
09 Apr 2024
Exploring Neural Network Landscapes: Star-Shaped and Geodesic
  Connectivity
Exploring Neural Network Landscapes: Star-Shaped and Geodesic Connectivity
Zhanran Lin
Puheng Li
Lei Wu
518
9
0
09 Apr 2024
Improving Model Fusion by Training-time Neuron Alignment with Fixed Neuron Anchors
Improving Model Fusion by Training-time Neuron Alignment with Fixed Neuron AnchorsIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
Zexi Li
Zhiqi Li
Jie Lin
Zhenyuan Zhang
Tao Lin
Chao Wu
Tao Lin
Chao Wu
469
5
0
02 Feb 2024
Disentangling Linear Mode-Connectivity
Disentangling Linear Mode-Connectivity
Gul Sena Altintas
Gregor Bachmann
Lorenzo Noci
Thomas Hofmann
451
9
0
15 Dec 2023
Proving Linear Mode Connectivity of Neural Networks via Optimal
  Transport
Proving Linear Mode Connectivity of Neural Networks via Optimal TransportInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Damien Ferbach
Baptiste Goujaud
Gauthier Gidel
Hadrien Hendrikx
MoMe
461
20
0
29 Oct 2023
Layer-wise Linear Mode Connectivity
Layer-wise Linear Mode ConnectivityInternational Conference on Learning Representations (ICLR), 2023
Linara Adilova
Maksym Andriushchenko
Michael Kamp
Asja Fischer
Martin Jaggi
FedMLFAttMoMe
589
21
0
13 Jul 2023
Distilled Pruning: Using Synthetic Data to Win the Lottery
Distilled Pruning: Using Synthetic Data to Win the Lottery
Luke McDermott
Daniel Cummings
SyDaDD
307
1
0
07 Jul 2023
Transferring Learning Trajectories of Neural Networks
Transferring Learning Trajectories of Neural NetworksInternational Conference on Learning Representations (ICLR), 2023
Daiki Chijiwa
335
4
0
23 May 2023
Phase diagram of early training dynamics in deep neural networks: effect
  of the learning rate, depth, and width
Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and widthNeural Information Processing Systems (NeurIPS), 2023
Dayal Singh Kalra
M. Barkeshli
352
18
0
23 Feb 2023
Revisiting Weighted Aggregation in Federated Learning with Neural
  Networks
Revisiting Weighted Aggregation in Federated Learning with Neural NetworksInternational Conference on Machine Learning (ICML), 2023
Zexi Li
Tao Lin
Xinyi Shang
Chao-Xiang Wu
FedML
427
114
0
14 Feb 2023
Class Interference of Deep Neural Networks
Class Interference of Deep Neural Networks
Dongcui Diao
Hengshuai Yao
Bei Jiang
197
1
0
31 Oct 2022
Git Re-Basin: Merging Models modulo Permutation Symmetries
Git Re-Basin: Merging Models modulo Permutation SymmetriesInternational Conference on Learning Representations (ICLR), 2022
Samuel K. Ainsworth
J. Hayase
S. Srinivasa
MoMe
1.1K
462
0
11 Sep 2022
FuNNscope: Visual microscope for interactively exploring the loss
  landscape of fully connected neural networks
FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks
Aleksandar Doknic
Torsten Moller
222
2
0
09 Apr 2022
Multirate Training of Neural Networks
Multirate Training of Neural NetworksInternational Conference on Machine Learning (ICML), 2021
Tiffany J. Vlaar
Benedict Leimkuhler
314
6
0
20 Jun 2021
1
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