Meta-Learning Loss Functions for Deep Neural Networks
<|reference_start|>Meta-Learning Loss Functions for Deep Neural Networks: 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.<|reference_end|>
@article{raymond2024meta-learning,
    title={Meta-Learning Loss Functions for Deep Neural Networks},
    author={Christian Raymond},
    journal={arXiv preprint arXiv:2406.09713},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09713},
    primaryClass={cs.LG cs.AI cs.NE}
}