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Revisiting "Qualitatively Characterizing Neural Network Optimization
  Problems"

Revisiting "Qualitatively Characterizing Neural Network Optimization Problems"

12 December 2020
Jonathan Frankle
ArXiv (abs)PDFHTML

Papers citing "Revisiting "Qualitatively Characterizing Neural Network Optimization Problems""

15 / 15 papers shown
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
377
5
0
20 Feb 2025
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
428
3
0
21 May 2024
TRAM: Bridging Trust Regions and Sharpness Aware Minimization
TRAM: Bridging Trust Regions and Sharpness Aware MinimizationInternational Conference on Learning Representations (ICLR), 2023
Tom Sherborne
Naomi Saphra
Pradeep Dasigi
Hao Peng
444
6
0
05 Oct 2023
Deep Model Fusion: A Survey
Deep Model Fusion: A Survey
Weishi Li
Yong Peng
Miao Zhang
Liang Ding
Han Hu
Li Shen
FedMLMoMe
346
106
0
27 Sep 2023
Plateau in Monotonic Linear Interpolation -- A "Biased" View of Loss
  Landscape for Deep Networks
Plateau in Monotonic Linear Interpolation -- A "Biased" View of Loss Landscape for Deep NetworksInternational Conference on Learning Representations (ICLR), 2022
Xiang Wang
Annie Wang
Mo Zhou
Rong Ge
MoMe
556
11
0
03 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
458
0
11 Sep 2022
On the Subspace Structure of Gradient-Based Meta-Learning
On the Subspace Structure of Gradient-Based Meta-Learning
Gustaf Tegnér
Alfredo Reichlin
Hang Yin
Mårten Björkman
Danica Kragic
364
0
0
08 Jul 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
216
2
0
09 Apr 2022
Fusing finetuned models for better pretraining
Fusing finetuned models for better pretraining
Leshem Choshen
Elad Venezian
Noam Slonim
Yoav Katz
FedMLAI4CEMoMe
428
115
0
06 Apr 2022
When Do Flat Minima Optimizers Work?
When Do Flat Minima Optimizers Work?Neural Information Processing Systems (NeurIPS), 2022
Jean Kaddour
Linqing Liu
Ricardo M. A. Silva
Matt J. Kusner
ODL
637
92
0
01 Feb 2022
Connecting Low-Loss Subspace for Personalized Federated Learning
Connecting Low-Loss Subspace for Personalized Federated Learning
S. Hahn
Minwoo Jeong
Junghye Lee
FedML
261
25
0
16 Sep 2021
What can linear interpolation of neural network loss landscapes tell us?
What can linear interpolation of neural network loss landscapes tell us?International Conference on Machine Learning (ICML), 2021
Tiffany J. Vlaar
Jonathan Frankle
MoMe
313
31
0
30 Jun 2021
Personalized Algorithm Generation: A Case Study in Learning ODE
  Integrators
Personalized Algorithm Generation: A Case Study in Learning ODE IntegratorsSIAM Journal on Scientific Computing (SISC), 2021
Yue Guo
Felix Dietrich
Tom S. Bertalan
Danimir T. Doncevic
Manuel Dahmen
Ioannis G. Kevrekidis
Qianxiao Li
501
13
0
04 May 2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss
  Landscapes
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas
Juhan Bae
Michael Ruogu Zhang
Stanislav Fort
R. Zemel
Roger C. Grosse
MoMe
552
32
0
22 Apr 2021
Learning Neural Network Subspaces
Learning Neural Network SubspacesInternational Conference on Machine Learning (ICML), 2021
Mitchell Wortsman
Maxwell Horton
Carlos Guestrin
Ali Farhadi
Mohammad Rastegari
UQCV
391
101
0
20 Feb 2021
1
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