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Neural collapse provides an elegant mathematical characterization of learned last layer representations (a.k.a.
Über den variabilitätsbereich der fourier’schen konstanten von positiven harmonischen funktionen
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Pre-training tasks for embedding-based large-scale retrieval
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Prevalence of neural collapse during the terminal phase of deep learning training
Fine-tuning can distort pretrained features and underperform out-of-distribution, 2022
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
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Principled and efficient transfer learning of deep models via neural collapse
Xiao Li, Sheng Liu, Jinxin Zhou, Xinyu Lu, Carlos Fernandez-Granda, Zhihui Zhu, and Qing Qu · 2022
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Neural collapse under cross-entropy loss
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Probabilistic machine learning: an introduction
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Memorization-dilation: Modeling neural collapse under noise
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Learning diverse and discriminative representations via the principle of maximal coding rate reduction
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Imbalance trouble: Revisiting neural-collapse geometry
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Extended unconstrained features model for exploring deep neural collapse
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Continual learning by modeling intra-class variation
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A study of neural collapse phenomenon: Grassmannian frame, symmetry, generalization, 2023
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Feature learning in deep classifiers through intermediate neural collapse
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Neural collapse with normalized features: A geometric analysis over the riemannian manifold, 2023
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