Machine Learning Modules for All Disciplines

2017 ◽  
Author(s):  
Thomas Way ◽  
Mary-Angela Papalaskari ◽  
Lillian Cassel ◽  
Paula Matuszek ◽  
Carol Weiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Modules

scholarly journals INFORMATION SYSTEMS OF INTEGRATED MACHINE LEARNING MODULES ON THE EXAMPLE OF A VERBAL ROBOT

2019 ◽  
Vol 6 (438) ◽  
pp. 215-222
Author(s):  
Maksat Kalimoldayev ◽  
◽  
Maxat Akhmetzhanov ◽  
Murat Kunelbayev ◽  
Talgat Sundetov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Learning Modules

Runtime Replacement of Machine Learning Modules in FPGA-Based Systems

2021 ◽  
Author(s):  
Dina G. Mahmoud ◽  
Beatrice Shokry ◽  
Abdallah Eirefaey ◽  
Hassanein H. Amer ◽  
Ihab Adly
Keyword(s):  
Machine Learning ◽  
Learning Modules

scholarly journals Deep Reinforcement Learning for Synthesizing Functions in Higher-Order Logic

10.29007/7jmg ◽  
2020 ◽  
Author(s):  
Thibault Gauthier
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Diophantine Equations ◽  
Search Space ◽  
Test Problems ◽  
Search Problem ◽  
Higher Order Logic ◽  
Tree Structures ◽  
Monte Carlo Tree Search ◽  
Learning Modules

The paper describes a deep reinforcement learning framework based on self-supervised learning within the proof assistant HOL4. A close interaction between the machine learning modules and the HOL4 library is achieved by the choice of tree neural networks (TNNs) as machine learning models and the internal use of HOL4 terms to represent tree structures of TNNs. Recursive improvement is possible when a task is expressed as a search problem. In this case, a Monte Carlo Tree Search (MCTS) algorithm guided by a TNN can be used to explore the search space and produce better examples for training the next TNN. As an illustration, term synthesis tasks on combinators and Diophantine equations are specified and learned. We achieve a success rate of 65% on combinator synthesis problems outperforming state-of-the-art ATPs run with their best general set of strategies. We set a precedent for statistically guided synthesis of Diophantine equations by solving 78.5% of the generated test problems.

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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