✳flâneur — a map of the web's best reading
Fourier Feature Networks
bmild.github.io · 827 words · saved by 1 readers
Project page for Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains.
Fourier Feature Networks --> --> Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains NeurIPS 2020 (spotlight) Matthew Tancik* UC Berkeley Pratul Srinivasan* UC Berkeley Ben Mildenhall* UC Berkeley Sara Fridovich-Keil UC Berkeley Nithin Raghavan UC Berkeley Utkarsh Singhal UC Berkeley Ravi Ramamoorthi UC San Diego Jonathan T. Barron Google Research Ren Ng UC Berkeley *denotes equal contribution Paper Code Abstract We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions
Explore this link on the map →saved by
related reading
- Feature Visualizationdistill.pub
- Feature-wise transformationsdistill.pub
- Neural Networks, Manifolds, and Topology -- colah's blogcolah.github.io
- Toy Models of Superpositiontransformer-circuits.pub
- Zoom In: An Introduction to Circuitsdistill.pub
- Some Math behind Neural Tangent Kernel | Lil'Loglilianweng.github.io
- Understanding the Neural Tangent Kernel – EigenTaleseigentales.com
- What Would Non-Linear Features Actually Look Like? — Liv Gortonlivgorton.com
- Feature Visualizationdistill.pub
- [2404.07200] Toward a Better Understanding of Fourier Neural Operators from a Spectral Perspectivearxiv.org
- [2404.19756] KAN: Kolmogorov-Arnold Networksarxiv.org
- Neural tangent kernel - Wikipediaen.wikipedia.org