Related papers: Meta-Learning Neural Procedural Biases

Meta-Learning Neural Procedural Biases

URL: http://arxiv.org/abs/2406.07983v1
Date: Wed, 12 Jun 2024 08:09:29 GMT
Title: Meta-Learning Neural Procedural Biases
Authors: Christian Raymond, Qi Chen, Bing Xue, Mengjie Zhan,
Abstract summary: We propose Neural Procedural Bias Meta-Learning, a novel framework designed to meta-learn task procedural biases. We 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.
Score: 9.876317838854018
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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