scholarly journals Graph Topology Inference Benchmarks for Machine Learning

2020 ◽  
Author(s):  
Carlos Lassance ◽  
Vincent Gripon ◽  
Gonzalo Mateos
Keyword(s):  
Machine Learning ◽  
Graph Topology ◽  
Topology Inference

scholarly journals Graph Topology Inference Based on Sparsifying Transform Learning

2019 ◽  
Vol 67 (7) ◽  
pp. 1712-1727 ◽  
Author(s):  
Stefania Sardellitti ◽  
Sergio Barbarossa ◽  
Paolo Di Lorenzo
Keyword(s):  
Graph Topology ◽  
Topology Inference

Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications

2020 ◽  
Vol 13 (4) ◽  
pp. 332-530
Author(s):  
Ljubiša Stanković ◽  
Danilo Mandic ◽  
Miloš Daković ◽  
Miloš Brajović ◽  
Bruno Scalzo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Graph Topology

scholarly journals Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering

2020 ◽  
Vol 29 ◽  
pp. 3183-3198 ◽  
Author(s):  
Siheng Chen ◽  
Chaojing Duan ◽  
Yaoqing Yang ◽  
Duanshun Li ◽  
Chen Feng ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Point Clouds ◽  
Graph Topology ◽  
3D Point Clouds ◽  
Topology Inference

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
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