output
20232026
most citedBreak It Down: Evidence for Structural Compositionality in Neural Networks

4 citations

15 papers

stat.ML2026

Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach

L{é}a Billet, Louise Trav{é}-Massuy{è}s, Elodie Chanthery +1

Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate future anomalies. This work int…

cs.LG2026

Scalable anomaly detection via a univariate Christoffel function

Florian Grivet, Didier Henrion, Jean-Bernard Lasserre +1

Anomaly detection plays a critical role in identifying unusual patterns across domains such as fraud detection, network intrusion, and system fault diagnosis. Recently, Christoffel…

cs.CL2026

GLeMM: A large-scale multilingual dataset for morphological research

Hathout Nabil, Basilio Calderone, Fiammetta Namer +1

In derivational morphology, what mechanisms govern the variation in form-meaning relations between words? The answers to this type of questions are typically based on intuition and…

cs.AI2026

Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling

Pierre Lotte, André Péninou, Olivier Teste

Reliable evaluation of anomaly detection methods in multivariate time series remains an open challenge, largely due to the limitations of existing benchmark datasets. Current resou…

q-bio.OT2025

Relation between in vitro microbial fermentations and in vivo performance in pigs selected for their residual feed intake

Olivier Zemb, Lauren Jouaron, Estelle Jordi +10

Bioinformatic analysis of microbiota revealed that certain metabolic pathways are associated with low- and high- residual feed intake (HRFI and LRFI), such as the amino-acid biosyn…

cs.LG2025

Busemann Functions in the Wasserstein Space: Existence, Closed-Forms, and Applications to Slicing

Clément Bonet, Elsa Cazelles, Lucas Drumetz +1

The Busemann function has recently found much interest in a variety of geometric machine learning problems, as it naturally defines projections onto geodesic rays of Riemannian man…