Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

Chayut Wiwatcharakoses

dblp:140/8091 · DBLP profile ↗
← Back
3ranked-venue papers
3as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Learning paradigms · 87% Deep learning architectures and training · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning paradigms › continual learning › catastrophic forgetting
catastrophic forgetting mitigation		 0.412019
Self-Organizing Incremental Neural Networks for Continual Learning · IJCAI 2019
Machine learning › Learning paradigms
continual learning		 0.412019
Self-Organizing Incremental Neural Networks for Continual Learning · IJCAI 2019
Machine learning › Deep learning architectures and training › biologically inspired neural network
self-organizing neural network		 0.112019
Self-Organizing Incremental Neural Networks for Continual Learning · IJCAI 2019

Methods — techniques the papers use, named apart from their topics

self-organizing incremental neural network · 0.4
YearPublicationVenuePosition
2021 A self-organizing incremental neural network for continual supervised learning
Chayut Wiwatcharakoses, Daniel P. Berrar
 Expert Syst. Appl.1
2020 SOINN+, a Self-Organizing Incremental Neural Network for Unsupervised Learning from Noisy Data Streams
Chayut Wiwatcharakoses, Daniel P. Berrar
 Expert Syst. Appl.1
2019 Self-Organizing Incremental Neural Networks for Continual Learning
abstract
Continual learning systems can adapt to new tasks, changes in data distributions, and new information that becomes incrementally available over time. The key challenge for such systems is how to mitigate catastrophic forgetting, i.e., how to prevent the loss of previously learned knowledge when new tasks need to be solved. In our research, we investigate self-organizing incremental neural networks (SOINN) for continual learning from both stationary and non-stationary data. We have developed a new algorithm, SOINN+, that learns to forget irrelevant nodes and edges and is robust to noise.
Chayut Wiwatcharakoses, Daniel P. Berrar
 IJCAI1