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Gradient Descent Provably Optimizes Over-parameterized Neural Networks., , , and . CoRR, (2018)Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon., and . CoRR, (2018)Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon., and . COLT, volume 99 of Proceedings of Machine Learning Research, page 2595-2623. PMLR, (2019)Gradient Descent Provably Optimizes Over-parameterized Neural Networks., , , and . ICLR (Poster), OpenReview.net, (2019)How Many Samples are Needed to Estimate a Convolutional Neural Network?, , , , , and . NeurIPS, page 371-381. (2018)Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints., , , and . CoRR, (2017)How Many Samples are Needed to Learn a Convolutional Neural Network?, , , , , and . CoRR, (2018)Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints., , , and . COLT, volume 75 of Proceedings of Machine Learning Research, page 605-638. PMLR, (2018)On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels., , and . COLT, volume 125 of Proceedings of Machine Learning Research, page 2683-2711. PMLR, (2020)SceneGen: Generative Contextual Scene Augmentation using Scene Graph Priors., , , , , and . CoRR, (2020)