Pages that link to "Item:Q1996787"
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The following pages link to Analysis of a two-layer neural network via displacement convexity (Q1996787):
Displaying 10 items.
- A selective overview of deep learning (Q2038303) (← links)
- Mean-field Langevin dynamics and energy landscape of neural networks (Q2077356) (← links)
- (Q5053253) (← links)
- The Continuous Formulation of Shallow Neural Networks as Wasserstein-Type Gradient Flows (Q5886422) (← links)
- Birth–death dynamics for sampling: global convergence, approximations and their asymptotics (Q6050829) (← links)
- A rigorous framework for the mean field limit of multilayer neural networks (Q6062704) (← links)
- A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling (Q6133439) (← links)
- Convergence rates for shallow neural networks learned by gradient descent (Q6137712) (← links)
- Stochastic gradient descent with noise of machine learning type. II: Continuous time analysis (Q6188971) (← links)
- The curse of overparametrization in adversarial training: precise analysis of robust generalization for random features regression (Q6550964) (← links)
