@inproceedings{02d5cd47973242be9dce9c30d68b850a,
title = "Resource-Constrained Knowledge Diffusion Processes Inspired by Human Peer Learning",
abstract = "We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources. The problem is motivated by the study of peer learning in human educational systems. In this context, we study natural knowledge diffusion processes in networks of interacting artificial learners. By 'natural', we mean processes that reflect human peer learning where the students' internal state and learning process is mostly opaque, and the main degree of freedom lies in the formation of peer learning groups by a coordinator who can potentially evaluate the learners before assigning them to peer groups. Among else, we empirically show that such processes indeed make effective use of the training resources, and enable the design of modular neural models that have the capacity to generalize without being prone to overfitting noisy labels.",
author = "Ehsan Beikihassan and Hoover, {Amy K.} and Ioannis Koutis and Ali Parviz and Niloofar Aghaieabiane",
note = "Publisher Copyright: {\textcopyright} 2023 The Authors.; 26th European Conference on Artificial Intelligence, ECAI 2023 ; Conference date: 30-09-2023 Through 04-10-2023",
year = "2023",
month = sep,
day = "28",
doi = "10.3233/FAIA230273",
language = "English (US)",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "214--222",
editor = "Kobi Gal and Kobi Gal and Ann Nowe and Nalepa, {Grzegorz J.} and Roy Fairstein and Roxana Radulescu",
booktitle = "ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings",
address = "Netherlands",
}