Kevin Tyloo

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it
• Scaling & Learning Theory — 11 citations into it
• Statistical and Nonlinear Physics — 8 citations into it
• Model Architectures — 5 citations into it

Whom Kevin Tyloo trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. E. Gardner

Papers from Kevin Tyloo's perspective

Ordered by how strongly the researchers Kevin Tyloo trusts endorse each paper. Same corpus, a different order for every researcher.

1. Neural Tangent Kernel: Convergence and Generalization in Neural Networks
2. A mean field view of the landscape of two-layer neural networks
3. High-dimensional dynamics of generalization error in neural networks
4. Gradient Descent Provably Optimizes Over-parameterized Neural Networks
5. Opening the Black Box of Deep Neural Networks via Information
6. Geometry of Optimization and Implicit Regularization in Deep Learning
7. Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error
8. Scaling description of generalization with number of parameters in deep learning
9. A jamming transition from under- to over-parametrization affects generalization in deep learning
10. MnasNet: Platform-Aware Neural Architecture Search for Mobile

Their papers

• Geometric compression of invariant manifolds in neural nets (2020) — 14 citations