Zobrazeno 1 - 10
of 2 494
pro vyhledávání: '"Pernot P"'
Autor:
Swami, R., Julie, G., Le-Denmat, S., Pernot, G., Singhal, D., Paterson, J., Maire, J., Motte, J. F., Paillet, N., Guillou, H., Gomes, S., Bourgeois, O.
Scanning Thermal Microscopy (SThM) has become an important measurement tool for characterizing the thermal properties of materials at the nanometer scale. This technique requires a SThM probe that combines an Atomic Force Microscopy (AFM) probe and a
Externí odkaz:
http://arxiv.org/abs/2403.05405
Autor:
Pernot, Pascal
Some popular Machine Learning Uncertainty Quantification (ML-UQ) calibration statistics do not have predefined reference values and are mostly used in comparative studies. In consequence, calibration is almost never validated and the diagnostic is le
Externí odkaz:
http://arxiv.org/abs/2403.00423
Autor:
Pernot, Pascal
Average calibration of the (variance-based) prediction uncertainties of machine learning regression tasks can be tested in two ways: one is to estimate the calibration error (CE) as the difference between the mean absolute error (MSE) and the mean va
Externí odkaz:
http://arxiv.org/abs/2402.10043
Autor:
Baccile, Niki, Poirier, Alexandre, Griel, Patrick Le, Pernot, Petra, Pala, Melike, Roelants, Sophie, Soetaert, Wim, Stevens, Christian
Publikováno v:
Colloids and Surfaces A: Physicochemical and Engineering Aspects
Sophorolipids are well-known scaled-up microbial glycolipid biosurfactants with a strong potential for commercialization due to their biological origin and mildness in contact with the skin and the environment compared to classical surfactants. Howev
Externí odkaz:
http://arxiv.org/abs/2310.14727
Autor:
Pernot, Pascal
Binwise Variance Scaling (BVS) has recently been proposed as a post hoc recalibration method for prediction uncertainties of machine learning regression problems that is able of more efficient corrections than uniform variance (or temperature) scalin
Externí odkaz:
http://arxiv.org/abs/2310.11978
Autor:
Pernot, Pascal
Publikováno v:
APL Mach. Learn. 1:046121 (2023)
Reliable uncertainty quantification (UQ) in machine learning (ML) regression tasks is becoming the focus of many studies in materials and chemical science. It is now well understood that average calibration is insufficient, and most studies implement
Externí odkaz:
http://arxiv.org/abs/2309.06240
Autor:
Baccile, Niki, Poirier, Alexandre, Perez, Javier, Pernot, Petra, Legriel, Patrick, Blesken, Christian C., Müller, Conrad, Blank, Lars, Tiso, Till
The structure-properties relationship of rhamnolipids, RLs, well known microbial bioamphiphiles (biosurfactants), is exlored in detail by coupling cryogenic transmission electron microscopy (cryo-TEM) and both ex situ and in situ small angle X-ray sc
Externí odkaz:
http://arxiv.org/abs/2306.14612
Autor:
Pernot, Pascal
Abstract Post hoc recalibration of prediction uncertainties of machine learning regression problems by isotonic regression might present a problem for bin-based calibration error statistics (e.g. ENCE). Isotonic regression often produces stratified u
Externí odkaz:
http://arxiv.org/abs/2306.05180
Autor:
Pernot, Pascal
The Expected Normalized Calibration Error (ENCE) is a popular calibration statistic used in Machine Learning to assess the quality of prediction uncertainties for regression problems. Estimation of the ENCE is based on the binning of calibration data
Externí odkaz:
http://arxiv.org/abs/2305.11905
Autor:
Papadacci, Clement, Finel, Victor, Provost, Jean, Villemain, Olivier, Bruneval, Patrick, Gennisson, Jean-Luc, Tanter, Mickael, Fink, Mathias, Pernot, Mathieu
Publikováno v:
Scientific Reports, 2017, 7 (1), pp.830
The assessment of myocardial fiber disarray is of major interest for the study of the progression of myocardial disease. However, time-resolved imaging of the myocardial structure remains unavailable in clinical practice. In this study, we introduce
Externí odkaz:
http://arxiv.org/abs/2304.09634