A study on meta learning optimization techniques

PH Sulibhavi, RA Hallyal, RK Katti… - 2022 IEEE 7th …, 2022 - ieeexplore.ieee.org
In the last decade, the domain of Artificial Intelligence (AI) has undergone rapid
advancement and has achieved ability to mimic near-human intelligence in multiple aspects …

Distributed learning for metaverse over wireless networks

X Liu, Y Liu - IEEE Communications Magazine, 2023 - ieeexplore.ieee.org
Metaverse is envisioned to be a human-centric framework that creates an interface for users
to immerse themselves in education, professional training, and entertainment by accessing …

Factorization machine based service recommendation on heterogeneous information networks

F Xie, L Chen, Y Ye, Z Zheng… - 2018 IEEE international …, 2018 - ieeexplore.ieee.org
With the wide adoption of SOA (Service Oriented Architecture), a massive amount of
innovative applications emerge in the Internet. One of the popular representations is …

A data-driven analysis of k-12 students' participation and learning performance on an online supplementary learning platform

J Zhang, X Gao, H Chen, L Chen… - 2021 International …, 2021 - ieeexplore.ieee.org
Due to the limitation of public school systems, many students pursue private supplementary
tutoring for improving their academic performance. Different from public schools, the private …

Few is enough: task-augmented active meta-learning for brain cell classification

P Yuan, A Mobiny, J Jahanipour, X Li… - … Image Computing and …, 2020 - Springer
Abstract Deep Neural Networks (or DNNs) must constantly cope with distribution changes in
the input data when the task of interest or the data collection protocol changes. Retraining a …

MGMASR: Multi-Graph and Multi-Aspect Neural Network for Service Recommendation in Internet of Services

Z Jia, Y Fan, J Zhang - IEEE Transactions on Network and …, 2023 - ieeexplore.ieee.org
With the flourishing development of Everything-as-a-Service (EaaS) and Internet of
Everything (IoE), Internet of Services (IoS) has recently emerged as a new buzzword in the …

[PDF][PDF] Human-centered machine learning in a social interaction assistant for individuals with visual impairments

V Balasubramanian, S Chakraborty… - … on Assistive Machine …, 2008 - cubic.asu.edu
Over the last couple of decades, the increasing focus on accessibility has resulted in the
design and development of several assistive technologies to aid people with visual …

A literature survey and empirical study of meta-learning for classifier selection

I Khan, X Zhang, M Rehman, R Ali - IEEE Access, 2020 - ieeexplore.ieee.org
Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

The impact of subjective technology adaptivity on the willingness of persons with disabilities to use emerging assistive technologies: A European perspective

A König, L Alčiauskaitė, T Hatzakis - International Conference on …, 2022 - Springer
Emerging digital technologies like augmented reality (AR) hold promising prospects for
people with disabilities. It remains, however, an open question how persons with disabilities …

Unintended machine learning biases as social barriers for persons with disabilitiess

B Hutchinson, V Prabhakaran, E Denton… - ACM SIGACCESS …, 2020 - dl.acm.org
Persons with disabilities face many barriers to full participation in society, and the rapid
advancement of technology has the potential to create ever more. Building equitable and …