  The goal of few-shot learning is to generalize and achieve high performance
on new unseen learning tasks, where each task has only a limited number of
examples available. Gradient-based meta-learning attempts to address this
challenging task by learning how to learn new tasks by embedding inductive
biases informed by prior learning experiences into the components of the
learning algorithm. In this work, we build upon prior research and propose
Neural Procedural Bias Meta-Learning (NPBML), a novel framework designed to
meta-learn task-adaptive procedural biases. Our approach aims to consolidate
recent advancements in meta-learned initializations, optimizers, and loss
functions by learning them simultaneously and making them adapt to each
individual task to maximize the strength of the learned inductive biases. This
imbues each learning task with a unique set of procedural biases which is
specifically designed and selected to attain strong learning performance in
only a few gradient steps. The experimental results show that by meta-learning
the procedural biases of a neural network, we can induce strong inductive
biases towards a distribution of learning tasks, enabling robust learning
performance across many well-established few-shot learning benchmarks.
