mlsanityData to 7 Oct 2026

Stop overkilling simple tasks with black-box models and use transparent models instead

arXiv 2302.02804 ↗First posted 6 Feb 2023cs.LG cs.CVcited by 0

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Abstract (arXiv)

In recent years, the employment of deep learning methods has led to several significant breakthroughs in artificial intelligence. Different from traditional machine learning models, deep learning-based approaches are able to extract features autonomously from raw data. This allows for bypassing the feature engineering process, which is generally considered to be both error-prone and tedious. Moreover, deep learning strategies often outperform traditional models in terms of accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 6 Feb 2023.

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References · 18

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6 more
  1. 2017Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradientscited by 236
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  3. 2020This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognitioncited by 50
  4. 2021What does LIME really see in images?cited by 21
  5. 2022A Theoretical Framework for AI Models Explainability with Application in Biomedicinecited by 7
  6. 2022Fruit Ripeness Classification: a Surveycited by 6

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