Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
A brief introduction to weakly supervised learning
ZH Zhou - National science review, 2018 - academic.oup.com
Supervised learning techniques construct predictive models by learning from a large
number of training examples, where each training example has a label indicating its ground …
number of training examples, where each training example has a label indicating its ground …
Community-based bayesian aggregation models for crowdsourcing
This paper addresses the problem of extracting accurate labels from crowdsourced datasets,
a key challenge in crowdsourcing. Prior work has focused on modeling the reliability of …
a key challenge in crowdsourcing. Prior work has focused on modeling the reliability of …
Human-agent collectives
NR Jennings, L Moreau, D Nicholson… - Communications of the …, 2014 - dl.acm.org
Human-agent collectives Page 1 80 COMMUNICATIONS OF THE ACM | DECEMBER 2014 |
VOL. 57 | NO. 12 review articles DOI:10.1145/2629559 HACs offer a new science for exploring …
VOL. 57 | NO. 12 review articles DOI:10.1145/2629559 HACs offer a new science for exploring …
Towards AI-powered personalization in MOOC learning
Abstract Massive Open Online Courses (MOOCs) represent a form of large-scale learning
that is changing the landscape of higher education. In this paper, we offer a perspective on …
that is changing the landscape of higher education. In this paper, we offer a perspective on …
Model assertions for monitoring and improving ml models
Abstract Machine learning models are increasingly deployed in mission-critical settings such
as vehicles, but unfortunately, these models can fail in complex ways. To prevent errors, ML …
as vehicles, but unfortunately, these models can fail in complex ways. To prevent errors, ML …
Toward efficient team formation for crowdsourcing in noncooperative social networks
Crowdsourcing has become a popular service computing paradigm for requesters to
integrate the ubiquitous human-intelligence services for tasks that are difficult for computers …
integrate the ubiquitous human-intelligence services for tasks that are difficult for computers …
On task assignment for real-time reliable crowdsourcing
I Boutsis, V Kalogeraki - 2014 IEEE 34th International …, 2014 - ieeexplore.ieee.org
With the rapid growth of mobile smartphone users, several commercial mobile companies
have exploited crowd sourcing as an effective approach to collect and analyze data, to …
have exploited crowd sourcing as an effective approach to collect and analyze data, to …
Brief survey of crowdsourcing for data mining
Crowdsourcing allows large-scale and flexible invocation of human input for data gathering
and analysis, which introduces a new paradigm of data mining process. Traditional data …
and analysis, which introduces a new paradigm of data mining process. Traditional data …
A real-time framework for task assignment in hyperlocal spatial crowdsourcing
Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of
collecting various types of spatial data. This method of data collection can significantly …
collecting various types of spatial data. This method of data collection can significantly …