# 500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)

arXiv: https://arxiv.org/abs/1802.05319 · HTML: https://mlsanity.com/paper/1802.05319/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 13 of 13)

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- [A Comprehensive Survey of Text Classification Techniques and Their Research Applications: Observational and Experimental Insights](https://mlsanity.com/paper/2401.12982/) (2024)
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- [Bridging Expert Knowledge with Deep Learning Techniques for Just-In-Time Defect Prediction](https://mlsanity.com/paper/2403.11079/) (2024)
- [Old but Gold: Reconsidering the value of feedforward learners for software analytics](https://mlsanity.com/paper/2101.06319/) (2021)
- [When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors](https://mlsanity.com/paper/2211.05920/) (2022)
- [Is Hyper-Parameter Optimization Different for Software Analytics?](https://mlsanity.com/paper/2401.09622/) (2024)
- [An Expert System for Redesigning Software for Cloud Applications](https://mlsanity.com/paper/2109.14569/) (2021)
- [Strong convexity-guided hyper-parameter optimization for flatter losses](https://mlsanity.com/paper/2402.05025/) (2024)
- [Predicting Question Quality on StackOverflow with Neural Networks](https://mlsanity.com/paper/2404.14449/) (2024)
- [Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review](https://mlsanity.com/paper/2504.11079/) (2025)

## Abstract (arXiv)

Deep learning methods are useful for high-dimensional data and are becoming widely used in many areas of software engineering. Deep learners utilizes extensive computational power and can take a long time to train-- making it difficult to widely validate and repeat and improve their results. Further, they are not the best solution in all domains. For example, recent results show that for finding related Stack Overflow posts, a tuned SVM performs similarly to a deep learner, but is significantly faster to train. This paper extends that recent result by clustering the dataset, then tuning very learners within each cluster. This approach is over 500 times faster than deep learning (and over 900 times faster if we use all the cores on a standard laptop computer). Significantly, this faster approach generates classifiers nearly as good (within 2% F1 Score) as the much slower deep learning method. Hence we recommend this faster methods since it is much easier to reproduce and utilizes far fewer CPU resources. More generally, we recommend that before researchers release research results, that they compare their supposedly sophisticated methods against simpler alternatives (e.g applying simpler learners to build local models).