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A jamming transition from under- to over-parametrization affects loss landscape and generalization., , , , , and . CoRR, (2018)Simulations of driven overdamped frictionless hard spheres., , and . Comput. Phys. Commun., 184 (3): 628-637 (2013)Scaling description of generalization with number of parameters in deep learning., , , , , , , , and . CoRR, (2019)Disentangling feature and lazy learning in deep neural networks: an empirical study., , , and . CoRR, (2019)Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data., , and . PLoS Computational Biology, (2010)The jamming transition as a paradigm to understand the loss landscape of deep neural networks., , , , , , and . CoRR, (2018)Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm., , and . CoRR, (2019)Comparing Dynamics: Deep Neural Networks versus Glassy Systems., , , , , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 324-333. PMLR, (2018)Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data., , and . CoRR, (2022)