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The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks.

, , and . COLT, volume 134 of Proceedings of Machine Learning Research, page 3889-3934. PMLR, (2021)

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On the Quality of the Initial Basin in Overspecified Neural Networks., and . ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 774-782. JMLR.org, (2016)The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks., , and . COLT, volume 134 of Proceedings of Machine Learning Research, page 3889-3934. PMLR, (2021)On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias., , and . NeurIPS, (2022)Depth Separations in Neural Networks: What is Actually Being Separated?, , and . COLT, volume 99 of Proceedings of Machine Learning Research, page 2664-2666. PMLR, (2019)Depth Separation in ReLU Networks for Approximating Smooth Non-Linear Functions., and . CoRR, (2016)Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks., and . ICML, volume 70 of Proceedings of Machine Learning Research, page 2979-2987. PMLR, (2017)How Many Neurons Does it Take to Approximate the Maximum?, , and . SODA, page 3156-3183. SIAM, (2024)On the Quality of the Initial Basin in Overspecified Neural Networks., and . CoRR, (2015)Spurious Local Minima are Common in Two-Layer ReLU Neural Networks., and . ICML, volume 80 of Proceedings of Machine Learning Research, page 4430-4438. PMLR, (2018)How Good is SGD with Random Shuffling?, and . COLT, volume 125 of Proceedings of Machine Learning Research, page 3250-3284. PMLR, (2020)