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Towards a Mathematical Understanding of Neural Network-Based Machine
  Learning: what we know and what we don't
v1v2v3 (latest)

Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't

CSIAM Transactions on Applied Mathematics (CSIAM Trans. Appl. Math.), 2020
22 September 2020
E. Weinan
Chao Ma
Stephan Wojtowytsch
Lei Wu
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't"

50 / 90 papers shown
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Convergence for adaptive resampling of random Fourier features
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A Spin Glass Characterization of Neural Networks
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10 Aug 2025
Sharp higher order convergence rates for the Adam optimizer
Sharp higher order convergence rates for the Adam optimizer
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Arnulf Jentzen
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349
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28 Apr 2025
Non-convergence to the optimal risk for Adam and stochastic gradient descent optimization in the training of deep neural networks
Non-convergence to the optimal risk for Adam and stochastic gradient descent optimization in the training of deep neural networks
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Robust Concept Erasure Using Task Vectors
Robust Concept Erasure Using Task Vectors
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Kelly O. Marshall
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Niv Cohen
505
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21 Feb 2025
A note on the physical interpretation of neural PDE's
A note on the physical interpretation of neural PDE's
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10 Feb 2025
High-dimensional classification problems with Barron regular boundaries under margin conditions
High-dimensional classification problems with Barron regular boundaries under margin conditionsNeural Networks (NN), 2024
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365
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Nonuniform random feature models using derivative information
Nonuniform random feature models using derivative information
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254
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Dimension-independent learning rates for high-dimensional classification
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Dimension-independent learning rates for high-dimensional classification problems
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Fast training of accurate physics-informed neural networks without gradient descent
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Score-based generative models break the curse of dimensionality in
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448
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Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field
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On Excess Risk Convergence Rates of Neural Network Classifiers
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Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss
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On the existence of optimal shallow feedforward networks with ReLU
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A Brief Survey on the Approximation Theory for Sequence Modelling
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Reinforcement Learning with Function Approximation: From Linear to
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Approximation results for Gradient Descent trained Shallow Neural
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Optimal bump functions for shallow ReLU networks: Weight decay, depth
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A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of
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