A framework for measuring the training efficiency of a neural architecture
<|reference_start|>A framework for measuring the training efficiency of a neural architecture: Measuring Efficiency in neural network system development is an open research problem. This paper presents an experimental framework to measure the training efficiency of a neural architecture. To demonstrate our approach, we analyze the training efficiency of Convolutional Neural Networks and Bayesian equivalents on the MNIST and CIFAR-10 tasks. Our results show that training efficiency decays as training progresses and varies across different stopping criteria for a given neural model and learning task. We also find a non-linear relationship between training stopping criteria, training Efficiency, model size, and training Efficiency.   Furthermore, we illustrate the potential confounding effects of overtraining on measuring the training efficiency of a neural architecture. Regarding relative training efficiency across different architectures, our results indicate that CNNs are more efficient than BCNNs on both datasets. More generally, as a learning task becomes more complex, the relative difference in training efficiency between different architectures becomes more pronounced.<|reference_end|>
@article{cueto-mendoza2024a,
    title={A framework for measuring the training efficiency of a neural
  architecture},
    author={Eduardo Cueto-Mendoza and John D. Kelleher},
    journal={arXiv preprint arXiv:2409.07925},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07925},
    primaryClass={cs.LG cs.AI}
}