Machine Learning Enabled Models to Predict Sulfur Solubility in Nuclear Waste Glasses

2021 ◽  
Author(s):  
Xinyi Xu ◽  
Taihao Han ◽  
Jie Huang ◽  
Albert A. Kruger ◽  
Aditya Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nuclear Waste ◽  
Sulfur Solubility

Machine learning to predict refractory corrosion during nuclear waste vitrification

MRS Advances ◽  
2021 ◽  
Author(s):  
Natalie J. Smith-Gray ◽  
Irmak Sargin ◽  
Scott Beckman ◽  
John McCloy
Keyword(s):  
Machine Learning ◽  
Nuclear Waste ◽  
Waste Vitrification ◽  
Refractory Corrosion

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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