English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We show that passing input points through a simple Fourier feature mappingenables a multilayer perceptron (MLP) to learn high-frequency functions inlow-dimensional problem domains. These results shed light on recent advances incomputer vision and graphics that achieve state-of-the-art results by usingMLPs to represent complex 3D objects and scenes. Using tools from the neuraltangent kernel (NTK) literature, we show that a standard MLP fails to learnhigh frequencies both in theory and in practice. To overcome this spectralbias, we use a Fourier feature mapping to transform the effective NTK into astationary kernel with a tunable bandwidth. We suggest an approach forselecting problem-specific Fourier features that greatly improves theperformance of MLPs for low-dimensional regression tasks relevant to thecomputer vision and graphics communities.

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains