Revisiting "Qualitatively Characterizing Neural Network Optimization Problems"

12 December 2020

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

9 / 9 papers shown

Title
High-dimensional manifold of solutions in neural networks: insights from statistical physics Enrico M. Malatesta 51 4 0 20 Feb 2025
Git Re-Basin: Merging Models modulo Permutation Symmetries Samuel K. Ainsworth J. Hayase S. Srinivasa MoMe 255 316 0 11 Sep 2022
FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks Aleksandar Doknic Torsten Moller 33 2 0 09 Apr 2022
Fusing finetuned models for better pretraining Leshem Choshen Elad Venezian Noam Slonim Yoav Katz FedML AI4CE MoMe 54 87 0 06 Apr 2022
When Do Flat Minima Optimizers Work? Jean Kaddour Linqing Liu Ricardo M. A. Silva Matt J. Kusner ODL 24 58 0 01 Feb 2022
Connecting Low-Loss Subspace for Personalized Federated Learning S. Hahn Minwoo Jeong Junghye Lee FedML 24 18 0 16 Sep 2021
What can linear interpolation of neural network loss landscapes tell us? Tiffany J. Vlaar Jonathan Frankle MoMe 30 27 0 30 Jun 2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes James Lucas Juhan Bae Michael Ruogu Zhang Stanislav Fort R. Zemel Roger C. Grosse MoMe 164 28 0 22 Apr 2021
Learning Neural Network Subspaces Mitchell Wortsman Maxwell Horton Carlos Guestrin Ali Farhadi Mohammad Rastegari UQCV 27 85 0 20 Feb 2021