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Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances.

, , , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 9722-9732. PMLR, (2021)

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Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances., , , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 9722-9732. PMLR, (2021)Arrows of Time for Large Language Models., , and . ICML, OpenReview.net, (2024)Kernel Alignment Risk Estimator: Risk Prediction from Training Data., , , , and . NeurIPS, (2020)Neural tangent kernel: convergence and generalization in neural networks (invited paper)., , and . STOC, page 6. ACM, (2021)Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity., , , and . NeurIPS, (2022)Implicit Regularization of Random Feature Models., , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 4631-4640. PMLR, (2020)Deep Linear Networks Dynamics: Low-Rank Biases Induced by Initialization Scale and L2 Regularization., , , , and . CoRR, (2021)Neural Tangent Kernel: Convergence and Generalization in Neural Networks., , and . NeurIPS, page 8580-8589. (2018)Freeze and Chaos: NTK views on DNN Normalization, Checkerboard and Boundary Artifacts., , , and . MSML, volume 190 of Proceedings of Machine Learning Research, page 257-270. PMLR, (2022)The asymptotic spectrum of the Hessian of DNN throughout training., , and . ICLR, OpenReview.net, (2020)