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Kymatio provides fast and efficient implementations of wavelet scattering transforms, a powerful tool for feature extraction in machine learning and signal processing. Designed for scalability and ease of use, Kymatio allows researchers and practitioners to leverage the benefits of wavelet scattering across various domains, including audio analysis, image recognition, and time series analysis.
Core development, research, and community engagement for the Kymatio library.
N/A - Primarily a distributed team leveraging shared resources and computational infrastructure.
Collaborative and open-source focused, emphasizing scientific rigor and community contributions.
Represents the central hub for the Kymatio project's intellectual leadership and strategic direction.
Kymatio's open-source nature means its code and applications are used globally by researchers and practitioners in various fields, including signal processing, image analysis, audio processing, and machine learning. It supports research, education, and industrial applications around the world.
N/A (Distributed Team)
N/A
N/A
France (Strong Presence)
Address: 45 rue d'Ulm, 75005 Paris, France (Approximate location, based on ENS address)
A center for theoretical and applied research within the Parisian scientific community.
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As of April 2025, Kymatio' leadership includes:
Kymatio has been backed by several prominent investors over the years, including:
Kymatio is primarily an open-source project driven by contributions from various researchers and developers. There aren't typical 'executive' hires or exits in the traditional company sense. Updates usually involve changes in core contributors or project leadership within collaborating institutions.
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Since Kymatio is an open-source project, there isn't a standardized company email format. Contacts are generally through personal academic or professional email addresses. Email addresses associated with individuals at the ENS (École Normale Supérieure) are common for core contributors.
[first_initial][last]@ens.fr (Example based on ENS)
Format
jdoe@ens.fr (Example only)
Example
N/A (Not applicable for an open-source project)%
Success rate
Journal of Machine Learning Research • October 2022
This paper introduces Group Equivariant Convolutional Networks (G-CNNs), which are built on group theory and can be used to extract features that are invariant to certain transformations. It shows how wavelet scattering transforms can be viewed as a special case of G-CNNs....more
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