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Renaud Bernatchez

dblp:324/1309 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

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

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

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
Efficient and distributed learning · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 50% Computational science and engineering · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
active learning		 0.612022
Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract) · AAAI 2022
Machine learning › Efficient and distributed learning › active learning
cost-sensitive active learning		 0.612022
Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract) · AAAI 2022
Computational science and engineering › pattern recognition
image classification		 0.212022
Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract) · AAAI 2022
Medical and health informatics
medical imaging		 0.212022
Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract) · AAAI 2022

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

deep learning · 1.1active learning · 1.1
YearPublicationVenuePosition
2022 Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)
abstract
Deep learning is a promising avenue to automate tedious analysis tasks in biomedical imaging. However, its application in such a context is limited by the large amount of labeled data required to train deep learning models. While active learning may be used to reduce the amount of labeling data, many approaches do not consider the cost of annotating, which is often significant in a biomedical imaging setting. In this work we show how annotation cost can be considered and learned during active learning on a classification task on the MNIST dataset.
Renaud Bernatchez, Audrey Durand, Flavie Lavoie-Cardinal
 AAAI1