Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple

2020 ◽  
pp. 330-333
Author(s):  
Matthew England ◽  
Dorian Florescu
Keyword(s):  
Machine Learning ◽  
Cylindrical Algebraic Decomposition

scholarly journals Using Machine Learning to Improve Cylindrical Algebraic Decomposition

2019 ◽  
Vol 13 (4) ◽  
pp. 461-488 ◽  
Author(s):  
Zongyan Huang ◽  
Matthew England ◽  
David J. Wilson ◽  
James Bridge ◽  
James H. Davenport ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cylindrical Algebraic Decomposition

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
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