Summary of Adamz: An Enhanced Optimisation Method For Neural Network Training, by Ilia Zaznov (department Of Computer Science et al.
AdamZ: An Enhanced Optimisation Method for Neural Network Training
by Ilia Zaznov, Atta Badii, Alfonso Dufour, Julian Kunkel
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 advanced variant of the Adam optimiser, AdamZ, enhances convergence efficiency in neural network training by dynamically adjusting the learning rate to address overshooting and stagnation. By reducing the learning rate when overshooting is detected and increasing it during periods of stagnation, AdamZ utilises hyperparameters like overshoot and stagnation factors, thresholds, and patience levels to guide these adjustments. While AdamZ may require slightly longer training times compared to some other optimisers, its consistency in minimising the loss function makes it particularly advantageous for applications where precision is critical. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AdamZ is an advanced tool that helps train neural networks more efficiently. It does this by adjusting how much the network learns at each step, so it doesn’t get stuck or learn too quickly. This helps AdamZ find the best way to train the network, which means it can do a better job of making predictions or understanding patterns in data. |
Keywords
» Artificial intelligence » Loss function » Neural network » Precision
