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Phase Diagram of Initial Condensation for Two-Layer Neural Networks
23866 1689 Pages:448-514 -
Understanding the Initial Condensation of Convolutional Neural Networks
8533 618 Pages:272-319 -
Enhanced Expressive Power and Fast Training of Neural Networks by Random Projections
41142 3506 Pages:532-550 -
Towards an Understanding of Residual Networks Using Neural Tangent Hierarchy (NTH)
293310 3110 Pages:692-760 -
BINet: Learn to Solve Partial Differential Equations with Boundary Integral Networks
35439 3289 Pages:275-305 -
Theory of the Frequency Principle for General Deep Neural Networks
44807 3700 Pages:484-507 -
Solving the Inverse Potential Problem in the Parabolic Equation by the Deep Neural Networks Method
13215 1274 Pages:852-883 -
A Deep Learning Approach for Solving the Inverse Problem of the Wave Equation
1079 186 Pages:29-61 -
Deep Ritz Methods for Laplace Equations with Dirichlet Boundary Condition
295288 3153 Pages:761-791 -
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks
21530 1934 Pages:350-389 -
Enforcing Exact Boundary and Initial Conditions in the Deep Mixed Residual Method
40873 3466 Pages:748-775 -
Non-Lipschitz Attack: A More Sparse Adversarial Attack via Non-Lipschitz $ℓ_p$ Regularization
29245 2616 Pages:797-819 -
Unsupervised Deep Learning Meets Chan-Vese Model
295215 3145 Pages:662-691 -
Model Meets Deep Learning in Image Inverse Problems
44341 4161 Pages:365-386 -
Hartley Spectral Pooling for Deep Learning
41692 3808 Pages:518-529 -
Failure-Informed Adaptive Sampling for PINNs, Part III: Applications to Inverse Problems
23315 1743 Pages:636-670
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