redwrasse

# -*- coding: utf-8 -*-
"""
standard discriminative gaussian
 y ~ N(f(x), sigma^2)

as well as heteroscedastic model
 y ~ N(f(x), sigma^2(x))

training on a dataset requiring the heteroscedastic model:
   x in R, y in R^2

redwrasse

# pseudocode impl

# Algorithm 1 Pseudocode in a PyTorch-like style.
# for x in loader: # x: batch with B sequences
# # Split image into patches
# # B x C x T x H x W -> B x C x T x N x h x w
# x = unfold(x, (patch_size, patch_size))
# x = spatial_jitter(x)
# # Embed patches (B x C x T x N)
# v = l2_norm(resnet(x))