5 citations · 6 across the 13 of their papers we have counts for
8 papers · 1 filter
The Latent Color Subspace: Emergent Order in High-Dimensional Chaos
Mateusz Pach, Jessica Bader, Quentin Bouniot +2
Text-to-image generation models have advanced rapidly, yet achieving fine-grained control over generated images remains difficult, largely due to limited understanding of how seman…
SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport
Simon Roschmann, Paul Krzakala, Sonia Mazelet +2
The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world. Recent work exploits thi…
TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
Khalid Oublal, Quentin Bouniot, Qi Gan +2
As black box models and pretrained models gain traction in time series applications, understanding and explaining their predictions becomes increasingly vital, especially in high-s…
Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers
Simon Roschmann, Quentin Bouniot, Vasilii Feofanov +2
Time series classification is a fundamental task in healthcare and industry, yet the development of time series foundation models (TSFMs) remains limited by the scarcity of publicl…
Mantis: Lightweight Foundation Model for Time Series Classification
Vasilii Feofanov, Songkang Wen, Shifeng Xie +10
While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly f…
Tailoring Mixup to Data for Calibration
Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
Among all data augmentation techniques proposed so far, linear interpolation of training samples, also called Mixup, has found to be effective for a large panel of applications. Al…