scholarly journals Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System

2021 ◽  
Author(s):  
Julian Arnold ◽  
Juan Carlos San Vicente Veliz ◽  
Debasish Koner ◽  
Narendra Singh ◽  
Raymond J. Bemish ◽  
...  
Keyword(s):  
Machine Learning ◽  
Product State ◽  
Collision System ◽  
Initial Reactant ◽  
Reactive Atom

scholarly journals Data-driven Analysis of Product State Propagation in Manufacturing Systems Using Visual Analytics and Machine Learning

Procedia CIRP ◽  
2020 ◽  
Vol 93 ◽  
pp. 449-454 ◽  
Author(s):  
Marc-André Filz ◽  
Sebastian Gellrich ◽  
Christoph Herrmann ◽  
Sebastian Thiede
Keyword(s):  
Machine Learning ◽  
Visual Analytics ◽  
Manufacturing Systems ◽  
Data Driven ◽  
Product State ◽  
State Propagation

Product state based view and machine learning: A suitable approach to increase quality?

2012 ◽  
Vol 45 (6) ◽  
pp. 1733-1738 ◽  
Author(s):  
C. Irgens ◽  
T. Wuest ◽  
K.-D. Thoben
Keyword(s):  
Machine Learning ◽  
Product State

scholarly journals Machine Learning for Observables: Reactant to Product State Distributions for Atom–Diatom Collisions

2020 ◽  
Vol 124 (35) ◽  
pp. 7177-7190 ◽  
Author(s):  
Julian Arnold ◽  
Debasish Koner ◽  
Silvan Käser ◽  
Narendra Singh ◽  
Raymond J. Bemish ◽  
...  
Keyword(s):  
Machine Learning ◽  
Product State

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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