Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI
Not on the map. Why?
Abstract (arXiv)
Historical materials are abundant. Yet, piecing together how human knowledge has evolved and spread both diachronically and synchronically remains a challenge that can so far only be very selectively addressed. The vast volume of materials precludes comprehensive studies, given the restricted number of human specialists. However, as large amounts of historical materials are now available in digital form there is a promising opportunity for AI-assisted historical analysis. In this work, we take a pivotal step towards analyzing vast historical corpora by employing innovative machine learning (ML) techniques, enabling in-depth historical insights on a grand scale. Our study centers on the evolution of knowledge within the `Sacrobosco Collection' -- a digitized collection of 359 early modern printed editions of textbooks on astronomy used at European universities between 1472 and 1650 -- roughly 76,000 pages, many of which contain astronomic, computational tables. An ML based analysis of these tables helps to unveil important facets of the spatio-temporal evolution of knowledge and innovation in the field of mathematical astronomy in the period, as taught at European universities.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 52 of its 243 references that are in the map. First posted on arXiv 13 Oct 2023.
Builds on
52 of 243 references.
References · 52
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
38 more
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,488
- 2014Deep Learning in Neural Networks: An Overviewcited by 770
- 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
- 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
- 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 463
- 2019General E(2)-Equivariant Steerable CNNscited by 446
- 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 419
- 2018A Survey on Deep Transfer Learningcited by 419
- 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 404
- 2015Return of Frustratingly Easy Domain Adaptationcited by 391
- 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 365
- 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
- 2021TrOCR: Transformer-based Optical Character Recognition with Pre-trained Modelscited by 279
- 2017Detecting causal associations in large nonlinear time series datasetscited by 244
- 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 234
- 2020Machine Learning Force Fieldscited by 207
- 2017State-of-the-art Speech Recognition With Sequence-to-Sequence Modelscited by 196
- 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 166
- 2020Higher-Order Explanations of Graph Neural Networks via Relevant Walkscited by 138
- 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 109
- 2014Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathwaycited by 94
- 2021Rethinking Text Line Recognition Modelscited by 45
- 2019Deep learning tools for the measurement of animal behavior in neurosciencecited by 44
- 2019Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variabilitycited by 44
- 2020Latin BERT: A Contextual Language Model for Classical Philologycited by 43
- 2020Building and Interpreting Deep Similarity Modelscited by 34
- 2019Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learningcited by 26
- 2019Restoring ancient text using deep learning: a case study on Greek epigraphycited by 25
- 2018Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Modelscited by 24
- 2020docExtractor: An off-the-shelf historical document element extractioncited by 20
- 2022A Survey of Historical Document Image Datasetscited by 20
- 2021ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier that Validates 301 New Exoplanetscited by 17
- 2021Neural OCR Post-Hoc Correction of Historical Corporacited by 12
- 2020Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networkscited by 11
- 2022Transformer-based HTR for Historical Documentscited by 10
- 2020Deciphering Undersegmented Ancient Scripts Using Phonetic Priorcited by 9
- 2021Topic Modeling Genre: An Exploration of French Classical and Enlightenment Dramacited by 9
50 most cited of 52 All 52 (JSON)
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.