Robin Vaysse

dblp:281/6785 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
2since 2021 · last 2021
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-author · 2 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 theory · 33% Time series and sequential data · 33% Learning paradigms · 33%

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

TopicWeightPapersLastEvidence papers
Machine learning › Learning paradigms
continual learning
0.512021
River: machine learning for streaming data in Python · J. Mach. Learn. Res. 2021
Machine learning › Learning theory
online learning
0.512021
River: machine learning for streaming data in Python · J. Mach. Learn. Res. 2021
Machine learning › Time series and sequential data
streaming data
0.512021
River: machine learning for streaming data in Python · J. Mach. Learn. Res. 2021
YearPublicationVenuePosition
2021 Automatic Extraction of Speech Rhythm Descriptors for Speech Intelligibility Assessment in the Context of Head and Neck Cancers
abstract
International audience
Robin Vaysse, Jérôme Farinas, Corine Astésano, Régine André-Obrecht
Interspeech1
2021 River: machine learning for streaming data in Python
abstract
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of two popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.
Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphaël Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet
J. Mach. Learn. Res.6