Meta-Learning Loss Functions for Deep Neural Networks

• URL: http://arxiv.org/abs/2406.09713v2
• Date: Sat, 29 Jun 2024 23:51:03 GMT
• Title: Meta-Learning Loss Functions for Deep Neural Networks
• Authors: Christian Raymond,
• Abstract summary: This thesis explores the concept of meta-learning to improve performance, through the often-overlooked component of the loss function. The loss function is a vital component of a learning system, as it represents the primary learning objective, where success is determined and quantified by the system's ability to optimize for that objective successfully.
• Score: 2.4258031099152735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans can often quickly and efficiently solve complex new learning tasks given only a small set of examples. In contrast, modern artificially intelligent systems often require thousands or millions of observations in order to solve even the most basic tasks. Meta-learning aims to resolve this issue by leveraging past experiences from similar learning tasks to embed the appropriate inductive biases into the learning system. Historically methods for meta-learning components such as optimizers, parameter initializations, and more have led to significant performance increases. This thesis aims to explore the concept of meta-learning to improve performance, through the often-overlooked component of the loss function. The loss function is a vital component of a learning system, as it represents the primary learning objective, where success is determined and quantified by the system's ability to optimize for that objective successfully.

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