M. De Castro & Umberto Zona - A vigotskijan perspective on machine learning. How cultural stereotypes are involved in education of algorithms

2022 ◽  
Author(s):  
Martina De Castro ◽  
Umberto Zona
Keyword(s):  
Machine Learning ◽  
Cultural Stereotypes

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

Stereotypes of Social Groups in Germany in Terms of Warmth and Competence

2010 ◽  
Vol 41 (2) ◽  
pp. 76-81 ◽  
Author(s):  
Frank Asbrock
Keyword(s):  
Gay Men ◽  
Social Groups ◽  
Social Judgment ◽  
Cultural Stereotypes ◽  
Student Sample ◽  
Content Model ◽  
Stereotype Content ◽  
German Student ◽  
Stable Clusters ◽  
Relevant Social Groups

The stereotype content model says that warmth and competence are fundamental dimensions of social judgment. This brief report analyzes the cultural stereotypes of relevant social groups in a German student sample (N = 82). In support of the model, stereotypes of 29 societal groups led to five stable clusters of differing warmth and competence evaluations. As expected, clusters cover all four possible combinations of warmth and competence. The study also reports unique findings for the German context, for example, similarities between the perceptions of Turks and other foreigners. Moreover, it points to different stereotypes of lesbians and gay men.

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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