Zobrazeno 1 - 10
of 4 069
pro vyhledávání: '"P, Saunier"'
We study algebraic K-theory and topological Hochschild homology in the setting of bimodules over a stable category, a datum we refer to as a laced category. We show that in this setting both K-theory and THH carry universal properties, the former def
Externí odkaz:
http://arxiv.org/abs/2411.04743
We propose a novel Transformer-based module to address the data association problem for multi-object tracking. From detections obtained by a pretrained detector, this module uses only coordinates from bounding boxes to estimate an affinity score betw
Externí odkaz:
http://arxiv.org/abs/2403.08018
Autor:
Sabri, Khalil, Djilali, Célia, Bilodeau, Guillaume-Alexandre, Saunier, Nicolas, Bouachir, Wassim
Publikováno v:
Proceedings of the 21st Conference on Robots and Vision, 2024
Urban traffic environments present unique challenges for object detection, particularly with the increasing presence of micromobility vehicles like e-scooters and bikes. To address this object detection problem, this work introduces an adapted detect
Externí odkaz:
http://arxiv.org/abs/2402.18503
Autor:
Saunier, Victor
We show that Quillen's resolution theorem for K-theory also applies to exact $\infty$-categories. We introduce heart structures on a stable $\infty$-category, generalizing weight structures, and using resolution ideas, we show that the category of st
Externí odkaz:
http://arxiv.org/abs/2311.13836
Autor:
Simon Deschamps
Publikováno v:
XVII-XVIII, Vol 74 (2017)
Externí odkaz:
https://doaj.org/article/c2120dd9837f46a4b65bd835ad74be76
Autor:
José Antonio Ferrer Benimeli
Publikováno v:
REHMLAC, Vol 5, Iss 1, Pp 226-236 (2013)
Externí odkaz:
https://doaj.org/article/52cc4162d86947d19c50b7d4a47545fe
Deep learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss function in a sequence-to-sequence setting, simply assuming that the erro
Externí odkaz:
http://arxiv.org/abs/2212.06653
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering, 2024
Spatiotemporal traffic data imputation is of great significance in intelligent transportation systems and data-driven decision-making processes. To perform efficient learning and accurate reconstruction from partially observed traffic data, we assert
Externí odkaz:
http://arxiv.org/abs/2212.01529
The problem of broad practical interest in spatiotemporal data analysis, i.e., discovering interpretable dynamic patterns from spatiotemporal data, is studied in this paper. Towards this end, we develop a time-varying reduced-rank vector autoregressi
Externí odkaz:
http://arxiv.org/abs/2211.15482