scholarly journals VP18.01: Machine learning applied to the standardised six‐step approach for placental localisation in basic obstetric ultrasound

2021 ◽  
Vol 58 (S1) ◽  
pp. 172-172
Author(s):  
A. Self ◽  
A. Gleed ◽  
S. Bhatnagar ◽  
A. Noble ◽  
A.T. Papageorghiou
Keyword(s):  
Machine Learning ◽  
Obstetric Ultrasound

scholarly journals VP34.21: Machine learning of the basic obstetric ultrasound examination: validation of the standardised six‐step approach

2020 ◽  
Vol 56 (S1) ◽  
pp. 202-202
Author(s):  
A. Self ◽  
Q. Chen ◽  
S. Bhatnagar ◽  
J.A. Noble ◽  
A.T. Papageorghiou
Keyword(s):  
Machine Learning ◽  
Ultrasound Examination ◽  
Obstetric Ultrasound

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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