  Conditional Neural Processes (CNPs) constitute a family of probabilistic
models that harness the flexibility of neural networks to parameterize
stochastic processes. Their capability to furnish well-calibrated predictions,
combined with simple maximum-likelihood training, has established them as
appealing solutions for addressing various learning problems, with a particular
emphasis on meta-learning. A prominent member of this family, Convolutional
Conditional Neural Processes (ConvCNPs), utilizes convolution to explicitly
introduce translation equivariance as an inductive bias. However, ConvCNP's
reliance on local discrete kernels in its convolution layers can pose
challenges in capturing long-range dependencies and complex patterns within the
data, especially when dealing with limited and irregularly sampled observations
from a new task. Building on the successes of Fourier neural operators (FNOs)
for approximating the solution operators of parametric partial differential
equations (PDEs), we propose Spectral Convolutional Conditional Neural
Processes (SConvCNPs), a new addition to the NPs family that allows for more
efficient representation of functions in the frequency domain.
