Preprint2025
Scientific machine learning
Unknown
This paper introduces Scientific Machine Learning (SciML), an interdisciplinary field merging scientific computing and machine learning to tackle complex modeling challenges.
0Jan 1, 2025
AI Research
AI & ML Research Papers
A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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6 papersPreprint2025
Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning
Unknown
This paper introduces Scientific Machine Learning (SciML) as an interdisciplinary field that synergistically combines physics-based models with data-driven approaches to advance research frontiers.
0Jan 1, 2025
PreprintArtificial Intelligence2021
What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
Markus Langer, Daniel Oster, Timo Speith, et al.
This paper reviews stakeholder desiderata for XAI and proposes a conceptual model to guide interdisciplinary XAI research.
600Feb 15, 2021
arXiv
PreprintJournal Of Big Data2020
CatBoost for big data: an interdisciplinary review
John Hancock, Taghi M. Khoshgoftaar
This paper provides the first interdisciplinary survey of CatBoost for big data, covering both its strengths and weaknesses in classification and regression tasks.
1.7kNov 4, 2020
PreprintElectronics2024
A Review of Large Language Models: Fundamental Architectures, Key Technological Evolutions, Interdisciplinary Technologies Integration, Optimization and Compression Techniques, Applications, and Challenges
Songyue Han, Mingyu Wang, Jialong Zhang, et al.
A comprehensive survey of LLMs covering architectures, key technologies, interdisciplinary integrations, optimization, applications, and challenges.
62Dec 21, 2024Large Language ModelsReinforcement Learning
PreprintInformation Fusion2024
Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
Luca Longo, Mario Brčić, Federico Cabitza, et al.
This paper presents a manifesto of 28 open challenges in Explainable AI, categorized into nine areas, to guide future interdisciplinary research.
551Feb 15, 2024
arXiv