Sort by
Refine Your Search
-
conditions. Conversely, data-driven approaches demonstrate strong performance in locomotion recognition and state estimation but frequently lack physical consistency, transparency, and robustness when exposed
-
machine learning for cybersecurity, current systems remain largely based on pattern recognition and struggle to incorporate contextual reasoning, temporal dependencies, and relationships between entities
-
pattern recognition, 2018, pp. 4510–4520. [7] Y. Cheng, D. Wang, P. Zhou, and T. Zhang, “Model compression and acceleration for deep neural networks: The principles, progress, and challenges,” IEEE Signal
-
architectures, with a strong focus on spiking neural networks (SNNs). 1 Abstract The increasing availability of spatio-temporal data has enabled significant advances in perception and pattern recognition systems
-
THE Interdisciplinary Science ranking, and top 51-100 for Data Science and Artificial Intelligence in the 2025 Shangaï ranking. IMT Atlantique is also awarded with the “Bienvenue en France” label, a recognition delivered
-
recruited candidate will contribute their expertise to the initial training of engineering students and master's students by teaching in the following areas: • Bioinformatics, • Machine learning and pattern
-
French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Le Rheu, Bretagne | France | 3 months ago
out your research at the Institute of Genetics, Environment, and Protection of Plants (IGEPP, https://igepp.rennes.hub.inrae.fr/ ), primarily based at the Le Rheu site near Rennes (35, France). The
-
Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
subject to significant inter-expert variability [3]. Automating fracture line identification would allow for more detailed and reproducible analysis of fracture patterns [4]. To date, models described in
-
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 3184–3194. [4] Y. Zeng, X. Zhang, H. Li, J. Wang, J. Zhang, and W. Zhou, “X 2-vlm: All-in-one pretrained model for vision