Metalearning Loss Functions For Deep Neural Networks

# Meta-Learning Loss Functions for Deep Neural Networks

**arXiv ID:** 2406.09713
**Authors:** Christian Raymond
**Published:** 2024-06-14T04:46:14Z
**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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## References

- [arXiv:2406.09713](http://arxiv.org/abs/2406.09713v3)