A framework for measuring the training efficiency of a neural architecture

• URL: http://arxiv.org/abs/2409.07925v1
• Date: Thu, 12 Sep 2024 10:45:38 GMT
• Title: A framework for measuring the training efficiency of a neural architecture
• Authors: Eduardo Cueto-Mendoza, John D. Kelleher,
• Abstract summary: This paper presents an experimental framework to measure the training efficiency of a neural architecture. We analyze the training efficiency of Convolutional Neural Networks and Bayesian equivalents on the MNIST and CIFAR-10 tasks.
• Score: 1.5373453926913085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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