Figure 1: Fourier features improve the results of coordinate-based MLPs for a variety of highfrequencylow-dimensional regression tasks, both with direct (b,c)and indirect (d,e)supervision.

Efficient methods that reliably quantify a deep neural network (DNN)'s predictive uncertainty are important for industrial-scale, real-world applications, which include examples such as object recognition in autonomous driving [22], ad click prediction in online advertising [76], and intent understanding inaconversational system [84].

Homomorphic encryption has recently attracted attention as a key solution to preserve privacy in machine learning applications.