Abstract
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.
Highlights
Machine learning (ML), in particular, deep learning (DL), has evolved rapidly due to the availability of huge computing power and big data and has proven to be successful in many applications such as image classification, natural language translation, etc
Using Differentiable Architecture Search (DARTS) and SPOS-Neural Architecture Search (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
We present the usefulness of the re-optimization of model weights with the sequentially connected with multiple trained ML models
Summary
Machine learning (ML), in particular, deep learning (DL), has evolved rapidly due to the availability of huge computing power and big data and has proven to be successful in many applications such as image classification, natural language translation, etc. In most ML approaches, a single task with a large model learned end-to-end is defined and trained to solve a given problem (see Fig. 1(a)) In most cases, this end-to-end approach provides state-of-the-art performance for a given problem in terms of precision and accuracy. Ideas for the connecting of sub-tasks and their model selection are presented
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