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pro vyhledávání: '"Cheung, Matt Y."'
Autor:
Cheung, Matt Y., Netherton, Tucker J., Court, Laurence E., Veeraraghavan, Ashok, Balakrishnan, Guha
Uncertainty quantification is crucial to account for the imperfect predictions of machine learning algorithms for high-impact applications. Conformal prediction (CP) is a powerful framework for uncertainty quantification that generates calibrated pre
Externí odkaz:
http://arxiv.org/abs/2410.05263
Autor:
Cheung, Matt Y, Netherton, Tucker J, Court, Laurence E, Veeraraghavan, Ashok, Balakrishnan, Guha
Recent advancements in machine learning have led to the development of novel medical imaging systems and algorithms that address ill-posed problems. Assessing their trustworthiness and understanding how to deploy them safely at test time remains an i
Externí odkaz:
http://arxiv.org/abs/2404.15274
Autor:
Alemohammad, Sina, Babaei, Hossein, Balestriero, Randall, Cheung, Matt Y., Humayun, Ahmed Imtiaz, LeJeune, Daniel, Liu, Naiming, Luzi, Lorenzo, Tan, Jasper, Wang, Zichao, Baraniuk, Richard G.
High dimensionality poses many challenges to the use of data, from visualization and interpretation, to prediction and storage for historical preservation. Techniques abound to reduce the dimensionality of fixed-length sequences, yet these methods ra
Externí odkaz:
http://arxiv.org/abs/2010.13975
Akademický článek
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Publikováno v:
IEEE transactions on biomedical circuits and systems [IEEE Trans Biomed Circuits Syst] 2024 Oct 16; Vol. PP. Date of Electronic Publication: 2024 Oct 16.
Autor:
Cheung MY; Department of Electrical & Computer Engineering, Rice University, Houston TX., Netherton TJ; Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston TX., Court LE; Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston TX., Veeraraghavan A; Department of Electrical & Computer Engineering, Rice University, Houston TX., Balakrishnan G; Department of Electrical & Computer Engineering, Rice University, Houston TX.
Publikováno v:
ArXiv [ArXiv] 2024 Jul 02. Date of Electronic Publication: 2024 Jul 02.