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Showing papers from Fraunhofer Institute for Algorithms and Scientific Computing Show all papers

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arXivFraunhofer Institute for AlgoritIn-context learning & prompting

Learning to Learn a Language

Prior-Fitted Language Model, trained solely on synthetic non-linguistic data, learns to infer and predict real languages from context with frozen weights, achieving strong cross-lingual compression and reasoning without ever seeing real text.

Lennart Carstens-Behrens, Holger Fröhlich

Published Oct 5, 2026 · ▲ 8 on Hugging Face · Code ★ 3

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 2/5
69%Highly rated
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The Journal of Prevention of Alzheimer s Disease 2026U BernKing's College LondonCentre for Healthy Brain AgeingFraunhofer Institute for AlgoritInformation Technologies InstituBiosignals & wearables

Screening for Alzheimer’s disease in the community using an AI-driven screening platform: design of the PREDICTOM study

PREDICTOM is a European observational cohort study developing an AI-driven platform to screen for Alzheimer's disease at home and in community settings using digital, physiological, and biofluid biomarkers.

Anna‐Katharine Brem, Zunera Khan, Jonas Radermacher, Kostas Georgiadis and 36 more

Published Mar 26, 2026 · 2 citations

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 4/5
medium 1/10
strict 1/5
78%Highly rated
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PreprintFraunhofer Institute for AlgoritU BonnBonn Aachen International CenterClinical NLP & EHR

Domain Adaptation Strategies for Transformer-Based Disease Prediction Using Electronic Health Records

Domain adaptation improves Ex-Med-BERT transfer across EHR institutions, outperforming fine-tuning with limited target data across six clinical endpoints.

Johanna Driever, Manuel Lentzen, Sumit Madan, Holger Fröhlich

Published Jun 2, 2025 · 0 citations

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
75%Highly rated
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iScience 2025Fraunhofer Institute for AlgoritHarvardMassachusetts General HospitalBerlin Institute of Health at ChUniversitätsmedizin GöttingenClinical NLP & EHR

On the fidelity versus privacy and utility trade-off of synthetic patient data

Synthetic patient data evaluation reveals non-DP models preserve fidelity and utility without privacy breaches, while DP disrupts correlations and k-anonymity risks exposure.

Tim Adams, Colin J. Birkenbihl, Karen Otte, Hwei Geok Ng and 6 more

Published Apr 14, 2025 · 24 citations

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10/21 AI panelreviewers recommend it

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AI panel: 10 of 21 reviewers recommend it
lenient 5/5
medium 4/11
strict 1/5