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Event Classification with Multi-step Machine Learning

arXiv 2106.02301 ↗First posted 4 Jun 2021cs.LGcited by 0

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Abstract (arXiv)

The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is presented. Pre-optimized ML models are connected and better performance is obtained by re-optimizing the connected one. The selection of an ML model from several small ML model candidates for each sub-task has been performed by using the idea based on Neural Architecture Search (NAS). In this paper, Differentiable Architecture Search (DARTS) and Single Path One-Shot NAS (SPOS-NAS) are tested, where the construction of loss functions is improved to keep all ML models smoothly learning. Using DARTS and SPOS-NAS as an optimization and selection as well as the connections for multi-step machine learning systems, we find that (1) such a system can quickly and successfully select highly performant model combinations, and (2) the selected models are consistent with baseline algorithms, such as grid search, and their outputs are well controlled.

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References · 16

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  9. 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 414
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  11. 2020A Comprehensive Survey of Neural Architecture Search: Challenges and Solutionscited by 254
  12. 2019Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluationcited by 230
4 more
  1. 2019Pareto Multi-Task Learningcited by 224
  2. 2017SMASH: One-Shot Model Architecture Search through HyperNetworkscited by 202
  3. 2019DetNAS: Backbone Search for Object Detectioncited by 81
  4. 2021An Improvement of Object Detection Performance using Multi-step Machine Learningscited by 1

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