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The recent work of Papyan, Han, & Donoho (2020) presented an intriguing "Neural Collapse" phenomenon, showing a structural property of interpolating classifiers in the late stage of training.
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Neural collapse under mse loss: Proximity to and dynamics on the central path
Han, X. Y., Papyan, V., and Donoho, D. L · 2021
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Prevalence of neural collapse during the terminal phase of deep learning training
Papyan, V., Han, X. Y., and Donoho, D. L · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Cited alongside, same era.
Wojtowytsch, S. et al · 2020
Cited alongside, same era.
How gradient descent separates data with neural collapse: A layer-peeled perspective
Ji, W., Lu, Y., Zhang, Y., Deng, Z., and Su, W. J
Cited in the paper.
An unconstrained layer-peeled perspective on neural collapse
Ji, W., Lu, Y., Zhang, Y., Deng, Z., and Su, W. J
Cited in the paper.
Explicit regularization and implicit bias in deep network classifiers trained with the square loss
Poggio, T. and Liao, Q
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Implicit dynamic regularization in deep networks
Poggio, T. and Liao, Q
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Towards an Empirical Theory of Deep Learning
Nakkiran, P · 2021
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Dynamics and neural collapse in deep classifiers trained with the square loss
Rangamani, A., Xu, M., Banburski, A., Liao, Q., and Poggio, T · 2021
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A geometric analysis of neural collapse with unconstrained features
Zhu, Z., Ding, T., Zhou, J., Li, X., You, C., Sulam, J., and Qu, Q · 2021
Later among the works it cites.