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Training deep neural networks for classification often includes minimizing the training loss beyond the zero training error point.
Matrix factorization techniques for recommender systems
Koren, Y., Bell, R., and Volinsky, C · 2009
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Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Hoffer, E., Hubara, I., and Soudry, D · 2017
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Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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The power of interpolation: Understanding the effectiveness of sgd in modern over-parametrized learning
Ma, S., Bassily, R., and Belkin, M · 2018
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Deep equilibrium models
Bai, S., Kolter, J. Z., and Koltun, V · 2019
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Does data interpolation contradict statistical optimality?
Belkin, M., Rakhlin, A., and Tsybakov, A. B · 2019
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Nonconvex optimization meets low-rank matrix factorization: An overview
Chi, Y., Lu, Y. M., and Chen, Y · 2019
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Neural collapse with unconstrained features
Mixon, D. G., Parshall, H., and Pi, J · 2020
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Prevalence of neural collapse during the terminal phase of deep learning training
Papyan, V., Han, X., and Donoho, D. L · 2020
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Continuous vs. discrete optimization of deep neural networks
Elkabetz, O. and Cohen, N · 2021
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Revealing the structure of deep neural networks via convex duality
Ergen, T. and Pilanci, M · 2021
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Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
Fang, C., He, H., Long, Q., and Su, W. J · 2021
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On the role of neural collapse in transfer learning
Galanti, T., György, A., and Hutter, M · 2021
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Dissecting supervised constrastive learning
Graf, F., Hofer, C., Niethammer, M., and Kwitt, R · 2021
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Evaluation of neural architectures trained with square loss vs cross-entropy in classification tasks
Hui, L. and Belkin, M · 2021
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An unconstrained layer-peeled perspective on neural collapse
Neural collapse under mse loss: Proximity to and dynamics on the central path
Han, X., Papyan, V., and Donoho, D. L · 2022
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A law of data separation in deep learning
He, H. and Su, W. J · 2022
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Neural collapse under cross-entropy loss
Lu, J. and Steinerberger, S · 2022
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Neural collapse in deep homogeneous classifiers and the role of weight decay
Rangamani, A. and Banburski-Fahey, A · 2022
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Imbalance trouble: Revisiting neural-collapse geometry
Thrampoulidis, C., Kini, G. R., Vakilian, V., and Behnia, T · 2022
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Extended unconstrained features model for exploring deep neural collapse
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Ji, W., Lu, Y., Zhang, Y., Deng, Z., and Su, W. J · 2021
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On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers
Wojtowytsch, S. et al · 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
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On the implicit bias towards minimal depth of deep neural networks
Galanti, T., Galanti, L., and Ben-Shaul, I · 2022
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J
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On the optimization landscape of neural collapse under mse loss: Global optimality with unconstrained features
Zhou, J., Li, X., Ding, T., You, C., Qu, Q., and Zhu, Z
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Tirer, T. and Bruna, J · 2022
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Do we really need a learnable classifier at the end of deep neural network?
Yang, Y., Xie, L., Chen, S., Li, X., Lin, Z., and Tao, D · 2022
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Neural collapse in deep linear network: From balanced to imbalanced data
Dang, H., Nguyen, T., Tran, T., Tran, H., and Ho, N · 2023
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Neural collapse: A review on modelling principles and generalization
Kothapalli, V · 2023
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