Summary of A Framework For Measuring the Training Efficiency Of a Neural Architecture, by Eduardo Cueto-mendoza and John D. Kelleher
A framework for measuring the training efficiency of a neural architecture
by Eduardo Cueto-Mendoza, John D. Kelleher
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an experimental framework to measure the training efficiency of neural architectures. The authors analyze the efficiency of Convolutional Neural Networks (CNNs) and their Bayesian equivalents on MNIST and CIFAR-10 tasks. Results show that training efficiency decays as training progresses, varying across different stopping criteria for a given model and task. A non-linear relationship is found between stopping criteria, model size, and training efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make neural networks train better. They tested different methods on some famous datasets (like pictures of numbers and animals) and found that the way you stop training affects how well it does. It’s like trying different ways to finish a puzzle – some work better than others! The results show that as you train, it gets harder for the network to learn, but some networks are better at learning than others. |
