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Recent years have witnessed the huge success of deep neural networks (DNNs) in various tasks of computer vision and text processing.
Multilayer feedforward networks are universal approximators
K. Hornik, M. Stinchcombe, and H. White · 1989
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A singular value thresholding algorithm for matrix completion
J.-F. Cai, E. J. Candès, and Z. Shen · 2010
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Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization
B. Recht, M. Fazel, and P. A. Parrilo · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Deep learning and the information bottleneck principle
N. Tishby and N. Zaslavsky · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Train longer, generalize better: closing the generalization gap in large batch training of neural networks
E. Hoffer, I. Hubara, and D. Soudry · 2017
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Neural tangent kernel: convergence and generalization in neural networks
A. Jacot, F. Gabriel, and C. Hongler · 2018
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
M. Belkin, D. Hsu, S. Ma, and S. Mandal · 2019
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Does data interpolation contradict statistical optimality?
M. Belkin, A. Rakhlin, and A. B. Tsybakov · 2019
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Another step toward demystifying deep neural networks
M. Elad, D. Simon, and A. Aberdam · 2020
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Neural collapse with unconstrained features
D. G. Mixon, H. Parshall, and J. Pi · 2020
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Prevalence of neural collapse during the terminal phase of deep learning training
V. Papyan, X. Han, and D. L. Donoho · 2020
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Revealing the structure of deep neural networks via convex duality
T. Ergen and M. Pilanci · 2021
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Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
C. Fang, H. He, Q. Long, and W. J. Su · 2021
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Neural collapse: A review on modelling principles and generalization
V. Kothapalli, E. Rasromani, and V. Awatramani · 2022
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Principled and efficient transfer learning of deep models via neural collapse
X. Li, S. Liu, J. Zhou, X. Lu, C. Fernandez-Granda, Z. Zhu, and Q. Qu · 2022
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Neural collapse under cross-entropy loss
J. Lu and S. Steinerberger · 2022
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Imbalance trouble: Revisiting neural-collapse geometry
C. Thrampoulidis, G. R. Kini, V. Vakilian, and T. Behnia · 2022
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Extended unconstrained features model for exploring deep neural collapse
T. Tirer and J. Bruna · 2022
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T. Galanti, A. György, and M. Hutter · 2021
Cited alongside, same era.
Neural collapse under MSE loss: Proximity to and dynamics on the central path
X. Han, V. Papyan, and D. L. Donoho · 2021
Cited alongside, same era.
An unconstrained layer-peeled perspective on neural collapse
W. Ji, Y. Lu, Y. Zhang, Z. Deng, and W. J. Su · 2021
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Explicit regularization and implicit bias in deep network classifiers trained with the square loss
T. Poggio and Q. Liao · 2021
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A geometric analysis of neural collapse with unconstrained features
Z. Zhu, T. Ding, J. Zhou, X. Li, C. You, J. Sulam, and Q. Qu · 2021
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On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers
W. E and S. Wojtowytsch · 2022
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Generalization bounds for transfer learning with pretrained classifiers
T. Galanti, A. György, and M. Hutter · 2022
Cited alongside, same era.
Do we really need a learnable classifier at the end of deep neural network?
Y. Yang, L. Xie, S. Chen, X. Li, Z. Lin, and D. Tao · 2022
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Neural collapse with normalized features: A geometric analysis over the riemannian manifold
C. Yaras, P. Wang, Z. Zhu, L. Balzano, and Q. Qu · 2022
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On the optimization landscape of neural collapse under mse loss: Global optimality with unconstrained features
J. Zhou, X. Li, T. Ding, C. You, Q. Qu, and Z. Zhu · 2022
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Are all losses created equal: A neural collapse perspective
J. Zhou, C. You, X. Li, K. Liu, S. Liu, Q. Qu, and Z. Zhu · 2022
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On the implicit geometry of cross-entropy parameterizations for label-imbalanced data
T. Behnia, G. R. Kini, V. Vakilian, and C. Thrampoulidis · 2023
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Neural collapse in deep linear network: From balanced to imbalanced data
H. Dang, T. Nguyen, T. Tran, H. Tran, and N. Ho · 2023
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Neural (tangent kernel) collapse
M. Seleznova, D. Weitzner, R. Giryes, G. Kutyniok, and H.-H. Chou · 2023
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Perturbation analysis of neural collapse
T. Tirer, H. Huang, and J. Niles-Weed · 2023
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