Search Results for author:
Found 6 papers, 2 papers with code
How to guess a gradient
We study how to narrow the gap in optimization performance between methods that calculate exact gradients and those that use directional derivatives.
Learning to Transform for Generalizable Instance-wise Invariance
However, too much or too little invariance can hurt, and the correct amount is unknown a priori and dependent on the instance.
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models
Multi-spectral imagery is invaluable for remote sensing due to different spectral signatures exhibited by materials that often appear identical in greyscale and RGB imagery.
Co-domain Symmetry for Complex-Valued Deep Learning
We study complex-valued scaling as a type of symmetry natural and unique to complex-valued measurements and representations.
HyperReal: Complex-Valued Layer Functions For Complex-Valued Scaling Invariance
SurReal complex-valued networks adopt a manifold view of complex numbers and derive a distance metric that is invariant to complex scaling.
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains.
