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2008.11245
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Deep Networks and the Multiple Manifold Problem
International Conference on Learning Representations (ICLR), 2020
25 August 2020
Sam Buchanan
D. Gilboa
John N. Wright
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Papers citing
"Deep Networks and the Multiple Manifold Problem"
33 / 33 papers shown
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Effects of Data Geometry in Early Deep Learning
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Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
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The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization
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A. Ayzenberg
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Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization
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Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction
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The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization
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Convergence and Implicit Bias of Gradient Flow on Overparametrized Linear Networks
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A Geometric Analysis of Neural Collapse with Unconstrained Features
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Tianyu Ding
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Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
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Cong Ma
Ashley Prater-Bennette
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Yuejie Chi
446
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Generalized Approach to Matched Filtering using Neural Networks
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Mariam Avagyan
R. Colgan
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Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
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