395 citations
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR55 papers
- CEA Paris-SaclayFR36 papers
- Centre National de la Recherche ScientifiqueFR30 papers
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- Astrophysique, Instrumentation et ModélisationFR13 papers
- Université Paris-SaclayFR13 papers
- Sorbonne UniversitéFR10 papers
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- Institut de Recherche en Astrophysique et PlanétologieFR6 papers
6 papers · 1 filter
Magnetic field tunable spectral response of kinetic inductance detectors
F. Levy-Bertrand, M. Calvo, U. Chowdhury +3
We tune the onset of optical response in aluminium kinetic inductance detectors from a natural cutoff frequency of 90 GHz to 60 GHz by applying an external magnetic field. The chan…
Discovery Potential of Future Electron-Positron Colliders for a 95 GeV Scalar
Pramod Sharma, Anza-Tshilidzi Mulaudzi, Karabo Mosala +7
The Large Electron Positron collider observed an indication for a new Higgs boson with a mass around \,GeV-\,GeV in the process with .…
When Knockoffs fail: diagnosing and fixing non-exchangeability of Knockoffs
Alexandre Blain, Angel Reyero Lobo, Julia Linhart +2
Knockoffs are a popular statistical framework that addresses the challenging problem of conditional variable selection in high-dimensional settings with statistical control. Such s…
On-ground calibration of the X-ray, gamma-ray, and relativistic electron detector onboard TARANIS
Yuuki Wada, Philippe Laurent, Damien Pailot +9
We developed the X-ray, Gamma-ray and Relativistic Electron detector (XGRE) onboard the TARANIS satellite, to investigate high-energy phenomena associated with lightning discharges…
Separating common from salient patterns with Contrastive Representation Learning
Robin Louiset, Edouard Duchesnay, Antoine Grigis +1
Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a…
Metric Learning Encoding Models: A Multivariate Framework for Interpreting Neural Representations
Louis Jalouzot, Christophe Pallier, Emmanuel Chemla +1
Understanding how explicit theoretical features are encoded in opaque neural systems is a central challenge now common to neuroscience and AI. We introduce Metric Learning Encoding…