Fetching the paper…
Reading the bibliography…
It has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances (Entezari et al., 2022).
Model Fusion via Optimal Transport, Feb. 2021
S. P. Singh and M. Jaggi · 1910
Earlier work this paper cites.
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness, July 2020
P. Zhao, P.-Y. Chen, P. Das, K. N. Ramamurthy, and X. Lin · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Linear Mode Connectivity in Multitask and Continual Learning, Oct. 2020
S. I. Mirzadeh, M. Farajtabar, D. Gorur, R. Pascanu, and H. Ghasemzadeh · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Weight uncertainty in neural network
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition, Apr. 2015
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Wide Residual Networks, June 2017
S. Zagoruyko and N. Komodakis · 2017
Earlier work this paper cites.
Essentially no barriers in neural network energy landscape
F. Draxler, K. Veschgini, M. Salmhofer, and F. Hamprecht · 2018
Cited alongside, same era.
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs, Oct. 2018
T. Garipov, P. Izmailov, D. Podoprikhin, D. Vetrov, and A. G. Wilson · 2018
Cited alongside, same era.
Using mode connectivity for loss landscape analysis
A. Gotmare, N. S. Keskar, C. Xiong, and R. Socher · 2018
Cited alongside, same era.
Densely Connected Convolutional Networks, Jan. 2018
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2018
Cited alongside, same era.
Linear connectivity reveals generalization strategies
J. Juneja, R. Bansal, K. Cho, J. Sedoc, and N. Saphra · 2022
Later among the works it cites.
Disentangling Linear Mode-Connectivity, Dec. 2023
G. S. Altintas, G. Bachmann, L. Noci, and T. Hofmann · 2023
Later among the works it cites.
Star-shaped space of solutions of the spherical negative perceptron
B. L. Annesi, C. Lauditi, C. Lucibello, E. M. Malatesta, G. Perugini, F. Pittorino, and L. Saglietti · 2023
Later among the works it cites.
Re-basin via implicit Sinkhorn differentiation
F. A. Guerrero Peña, H. R. Medeiros, T. Dubail, M. Aminbeidokhti, E. Granger, and M. Pedersoli · 2023
Later among the works it cites.
Repair: Renormalizing permuted activations for interpolation repair, 2023
K. Jordan, H. Sedghi, O. Saukh, R. Entezari, and B. Neyshabur · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Brea, B. Simsek, B. Illing, and W. Gerstner · 2019
Cited alongside, same era.
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
R. Kuditipudi, X. Wang, H. Lee, Y. Zhang, Z. Li, W. Hu, R. Ge, and S. Arora · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
Cited alongside, same era.
Linear mode connectivity and the lottery ticket hypothesis
J. Frankle, G. K. Dziugaite, D. Roy, and M. Carbin · 2020
Cited alongside, same era.
Federated learning with matched averaging
H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni · 2020
Cited alongside, same era.
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
G. Benton, W. Maddox, S. Lotfi, and A. G. G. Wilson · 2021
Cited alongside, same era.
Git Re-Basin: Merging Models modulo Permutation Symmetries, Dec. 2022
S. K. Ainsworth, J. Hayase, and S. Srinivasa · 2022
Cited alongside, same era.
Random initialisations performing above chance and how to find them, Nov. 2022
F. Benzing, S. Schug, R. Meier, J. von Oswald, Y. Akram, N. Zucchet, L. Aitchison, and A. Steger · 2022
Cited alongside, same era.
Ffcv: Accelerating training by removing data bottlenecks
G. Leclerc, A. Ilyas, L. Engstrom, S. M. Park, H. Salman, and A. Mądry · 2023
Later among the works it cites.
Exploring diversified adversarial robustness in neural networks via robust mode connectivity
R. Wang, Y. Li, and S. Liu · 2023
Later among the works it cites.
Optimizing Mode Connectivity for Class Incremental Learning
H. Wen, H. Cheng, H. Qiu, L. Wang, L. Pan, and H. Li · 2023
Later among the works it cites.
A compact representation for bayesian neural networks by removing permutation symmetry
T. Z. Xiao, W. Liu, and R. Bamler · 2023
Later among the works it cites.
Proving linear mode connectivity of neural networks via optimal transport
D. Ferbach, B. Goujaud, G. Gidel, and A. Dieuleveut · 2024
Closest in time.
Exploring neural network landscapes: Star-shaped and geodesic connectivity
Z. Lin, P. Li, and L. Wu · 2024
Closest in time.
Simultaneous linear connectivity of neural networks modulo permutation
E. Sharma, D. Kwok, T. Denton, D. M. Roy, D. Rolnick, and G. K. Dziugaite · 2024
Closest in time.