Summary of Shaping History: Advanced Machine Learning Techniques For the Analysis and Dating Of Cuneiform Tablets Over Three Millennia, by Danielle Kapon et al.
Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia
by Danielle Kapon, Michael Fire, Shai Gordin
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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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 The paper presents a machine learning approach to classify ancient Mesopotamian cuneiform tablets based on their silhouettes, leveraging a large dataset from the Cuneiform Digital Library Initiative. The researchers apply deep learning methods, including Variational Auto-Encoders (VAEs), to develop a classification model that achieves a 61% macro F1-score for tablet silhouettes. This work contributes to document analysis and diplomatics by demonstrating the value of combining large-scale data analysis with statistical methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses machine learning to help historians and epigraphists understand ancient Mesopotamian cuneiform tablets better. It takes pictures of over 94,000 clay tablets from different times in history and teaches a computer to recognize patterns in these shapes that can tell us which time period they belong to. The researchers use special computer programs called Variational Auto-Encoders (VAEs) to do this. They also make tools that help people understand why certain patterns are important for figuring out when the tablets were made. |
Keywords
» Artificial intelligence » Classification » Deep learning » F1 score » Machine learning
