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Charles Bourbeau

dblp:352/9744 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
—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
Learning paradigms · 44% Time series and sequential data · 44% Representation and self-supervised learning · 13%

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

TopicWeightPapersLastEvidence papers
Machine learning › Time series and sequential data › non-stationary environments
concept drift		 0.712023
Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract) · AAAI 2023
Machine learning › Learning paradigms
incremental learning		 0.712023
Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract) · AAAI 2023
Machine learning › Representation and self-supervised learning › latent space
latent space analysis		 0.212023
Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract) · AAAI 2023

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

latent space visualization · 0.7
YearPublicationVenuePosition
2023 Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)
abstract
This work investigates the evolution of latent space when deep learning models are trained incrementally in non-stationary environments that stem from concept drift. We propose a methodology for visualizing the incurred change in latent representations. We further show that classes not targeted by concept drift can be negatively affected, suggesting that the observation of all classes during learning may regularize the latent space.
Charles Bourbeau, Audrey Durand
 AAAI1