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pro vyhledávání: '"Lensen A"'
Manifold learning techniques play a pivotal role in machine learning by revealing lower-dimensional embeddings within high-dimensional data, thus enhancing both the efficiency and interpretability of data analysis by transforming the data into a lowe
Externí odkaz:
http://arxiv.org/abs/2403.14139
Genetic programming (GP) has the potential to generate explainable results, especially when used for dimensionality reduction. In this research, we investigate the potential of leveraging eXplainable AI (XAI) and large language models (LLMs) like Cha
Externí odkaz:
http://arxiv.org/abs/2403.03397
Autor:
Zeng, Peng, Song, Xiaotian, Lensen, Andrew, Ou, Yuwei, Sun, Yanan, Zhang, Mengjie, Lv, Jiancheng
Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable machine learning (ML). Genetic programming (GP) has been the dominator in
Externí odkaz:
http://arxiv.org/abs/2304.08915
This report investigates an unsupervised, feature-based image matching pipeline for the novel application of identifying individual k\=ak\=a. Applied with a similarity network for clustering, this addresses a weakness of current supervised approaches
Externí odkaz:
http://arxiv.org/abs/2301.06678
The judiciary has historically been conservative in its use of Artificial Intelligence, but recent advances in machine learning have prompted scholars to reconsider such use in tasks like sentence prediction. This paper investigates by experimentatio
Externí odkaz:
http://arxiv.org/abs/2208.06981
Publikováno v:
In Journal of Cleaner Production 25 November 2024 481
Autor:
Wilkinson, Jack, Heal, Calvin, Antoniou, George A., Flemyng, Ella, Avenell, Alison, Barbour, Virginia, Bordewijk, Esmee M., Brown, Nicholas J.L., Clarke, Mike, Dumville, Jo, Grohmann, Steph, Gurrin, Lyle C., Hayden, Jill A., Hunter, Kylie E., Lam, Emily, Lasserson, Toby, Li, Tianjing, Lensen, Sarah, Liu, Jianping, Lundh, Andreas, Meyerowitz-Katz, Gideon, Mol, Ben W., O'Connell, Neil E., Parker, Lisa, Redman, Barbara, Seidler, Anna Lene, Sheldrick, Kyle, Sydenham, Emma, Dahly, Darren L., van Wely, Madelon, Bero, Lisa, Kirkham, Jamie J.
Publikováno v:
In Journal of Clinical Epidemiology November 2024 175
Autor:
Yuen-Yan Chang, Camila Valenzuela, Arthur Lensen, Noelia Lopez-Montero, Saima Sidik, John Salogiannis, Jost Enninga, John Rohde
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Intracellular bacterial pathogens gain entry to mammalian cells inside a vacuole derived from the host membrane. Some of them escape the bacteria-containing vacuole (BCV) and colonize the cytosol. Bacteria replicating within BCVs coopt the m
Externí odkaz:
https://doaj.org/article/b50b0625dde648359113f818c3e4dba2
CRISPR-Cas9 screening reveals a distinct class of MHC-I binders with precise HLA-peptide recognition
Autor:
Tom A.W. Schoufour, Anneloes van der Plas - van Duijn, Ian Derksen, Marije Melgers, Jacqueline M.F. van Veenendaal, Claire Lensen, Mirjam H.M. Heemskerk, Jacques Neefjes, Ruud H.M. Wijdeven, Ferenc A. Scheeren
Publikováno v:
iScience, Vol 27, Iss 6, Pp 110120- (2024)
Summary: Human leukocyte antigen (HLA) class-I molecules present fragments of the cellular proteome to the T cell receptor (TCR) of cytotoxic T cells to control infectious diseases and cancer. The large number of combinations of HLA class-I allotypes
Externí odkaz:
https://doaj.org/article/4f8712afc8ef4d058388bf0791f5c4b4
Manifold learning methods are an invaluable tool in today's world of increasingly huge datasets. Manifold learning algorithms can discover a much lower-dimensional representation (embedding) of a high-dimensional dataset through non-linear transforma
Externí odkaz:
http://arxiv.org/abs/2108.09914