scholarly journals Accelerating graph sampling for graph machine learning using GPUs

2021 ◽  
Author(s):  
Abhinav Jangda ◽  
Sandeep Polisetty ◽  
Arjun Guha ◽  
Marco Serafini
Keyword(s):  
Machine Learning ◽  
Graph Sampling

Exploring Video Sharing Websites Content with Machine Learning

2014 ◽  
Vol 5 (4) ◽  
pp. 31-50
Author(s):  
Nan Zhao ◽  
Löic Baud ◽  
Patrick Bellot
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Online Video ◽  
Video Content ◽  
Cultural Goods ◽  
Content Category ◽  
Graph Sampling ◽  
Video Sharing ◽  
Category System ◽  
Internet Users

This article studies the characteristics of content on video sharing websites. A better understanding on online video content can help to analyse Internet users' behaviour and improve the video-sharing service. We improved an existing graph-sampling algorithm so that it could be more adapted to sample over the video sharing websites. A newly category system is defined in this paper, which can be applied on many different video sharing websites for content analysis. We also implement machine learning to realize the content re-classification with the newly defined category system. The efficiency reaches at 90%. From the classified content analysis, we find the content category distribution is not constant, and nowadays, cultural goods content take about 70% over all the sampled videos.

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

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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