Using Machine Learning to Predict Path-Based Slack from Graph-Based Timing Analysis

2018 ◽  
Author(s):  
Andrew B. Kahng ◽  
Uday Mallappa ◽  
Lawrence Saul
Keyword(s):  
Machine Learning ◽  
Timing Analysis

MLTimer: Leakage Power Minimization in Digital Circuits Using Machine Learning and Adaptive Lazy Timing Analysis

2018 ◽  
Vol 14 (2) ◽  
pp. 285-301 ◽  
Author(s):  
S. L. P. S. K. Patanjali ◽  
Milan Patnaik ◽  
Seetal Potluri ◽  
V. Kamakoti
Keyword(s):  
Machine Learning ◽  
Digital Circuits ◽  
Timing Analysis ◽  
Leakage Power ◽  
Power Minimization

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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