Dynamically Typed

SIREN

DT #42 — June 21, 2020

Vincent Sitzmann et al. introduced SIREN, a new activation function for implicit neural representations, a technique to encode a signal (e.g. an image, audio sample, video clip, or 3D scenes) in the parameters of a neural network. Their main innovation is using a periodic activation function (based on a sine wave) instead of the usual ReLU, TanH, or Softplus nonlinearities, which yields very impressive results. Check out their paper video and demo site.

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ML Research

Recent models, datasets, and tools for machine learning research.

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