Concept
Classification et catégorisation de documents
Publications associées (226)
Post-correction of Historical Text Transcripts with Large Language Models: An Exploratory Study
Frédéric Kaplan, Maud Ehrmann, Matteo Romanello, Sven-Nicolas Yoann Najem, Emanuela Boros
The quality of automatic transcription of heritage documents, whether from printed, manuscripts or audio sources, has a decisive impact on the ability to search and process historical texts. Although significant progress has been made in text recognition ( ...
The Association for Computational Linguistics2024Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance
Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro, Xiaolong Lu
Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...
Ieee-Inst Electrical Electronics Engineers Inc2024From Archival Sources to Structured Historical Information: Annotating and Exploring the "Accordi dei Garzoni"
Frédéric Kaplan, Maud Ehrmann, Orlin Biserov Topalov
If automatic document processing techniques have achieved a certain maturity for present time documents, the transformation of hand-written documents into well-represented, structured and connected data which can satisfactorily be used for historical study ...
Routledge, Taylor & Francis Group2023The Virtue of Complexity in Return Prediction
Much of the extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in ...
Hoboken2023Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring
Christian Leinenbach, Sergey Shevchik, Rafal Wróbel, Marc Leparoux
This study presents a self-supervised Bayesian Neural Network (BNN) framework using air-borne Acoustic Emission (AE) to identify different Laser Powder Bed Fusion (LPBF) process regimes such as Lack of Fusion, conduction mode, and keyhole without ground-tr ...
London2023TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation
Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar
Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...
IEEE2023Estimating and Improving the Robustness of Attributions in Text
End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...
EPFL2023Lausanne Historical Censuses Dataset HTR 35k
Lucas Arnaud André Rappo, Rémi Guillaume Petitpierre, Marion Kramer
This training dataset includes a total of 34,913 manually transcribed text segments. It is dedicated to the handwritten text recognition (HTR) of historical sources, typically tabular records, such as censuses. This dataset is based on a sample of 83 pages ...
Zenodo2023Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations
Devis Tuia, Olga Fink, Florent Evariste Forest, Hugo Laurent Pascal Porta
Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe h ...
Research Publishing2023Textual Explanations and Critiques in Recommendation Systems
Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...
EPFL2022