Zobrazeno 1 - 10
of 13 279
pro vyhledávání: '"P Christel"'
The enormous growth of the complexity of modern computer systems leads to an increasing demand for techniques that support the comprehensibility of systems. This has motivated the very active research field of formal methods that enhance the understa
Externí odkaz:
http://arxiv.org/abs/2412.05162
Contrastive learning has significantly improved representation quality, enhancing knowledge transfer across tasks in continual learning (CL). However, catastrophic forgetting remains a key challenge, as contrastive based methods primarily focus on "s
Externí odkaz:
http://arxiv.org/abs/2412.02865
We present the architecture of a fully autonomous, bio-inspired cognitive agent built around a spiking neural network (SNN) implementing the agent's semantic memory. The agent explores its universe and learns concepts of objects/situations and of its
Externí odkaz:
http://arxiv.org/abs/2411.12308
Upholding data privacy especially in medical research has become tantamount to facing difficulties in accessing individual-level patient data. Estimating mixed effects binary logistic regression models involving data from multiple data providers like
Externí odkaz:
http://arxiv.org/abs/2411.04002
Autor:
Sirocchi, Christel, Suffian, Muhammad, Sabbatini, Federico, Bogliolo, Alessandro, Montagna, Sara
In clinical practice, decision-making relies heavily on established protocols, often formalised as rules. Concurrently, Machine Learning (ML) models, trained on clinical data, aspire to integrate into medical decision-making processes. However, despi
Externí odkaz:
http://arxiv.org/abs/2411.03105
Autor:
Ruparell, Karan, Marks, Robert J., Wood, Andy, Hunt, Kieran M. R., Cloke, Hannah L., Prudhomme, Christel, Pappenberger, Florian, Chantry, Matthew
Long Short Term Memory networks (LSTMs) are used to build single models that predict river discharge across many catchments. These models offer greater accuracy than models trained on each catchment independently if using the same data. However, the
Externí odkaz:
http://arxiv.org/abs/2410.16343
Autor:
Musabini, Antonyo, Novikov, Ivan, Soula, Sana, Leonet, Christel, Wang, Lihao, Benmokhtar, Rachid, Burger, Fabian, Boulay, Thomas, Perrotton, Xavier
Current parking area perception algorithms primarily focus on detecting vacant slots within a limited range, relying on error-prone homographic projection for both labeling and inference. However, recent advancements in Advanced Driver Assistance Sys
Externí odkaz:
http://arxiv.org/abs/2408.12575
In medical research, individual-level patient data provide invaluable information, but the patients' right to confidentiality remains of utmost priority. This poses a huge challenge when estimating statistical models such as linear mixed models, whic
Externí odkaz:
http://arxiv.org/abs/2407.20796
Autor:
Held, Jan, Cioppa, Anthony, Giancola, Silvio, Hamdi, Abdullah, Devue, Christel, Ghanem, Bernard, Van Droogenbroeck, Marc
Over the past decade, the technology used by referees in football has improved substantially, enhancing the fairness and accuracy of decisions. This progress has culminated in the implementation of the Video Assistant Referee (VAR), an innovation tha
Externí odkaz:
http://arxiv.org/abs/2407.12483
This paper studies various notions of approximate probabilistic bisimulation on labeled Markov chains (LMCs). We introduce approximate versions of weak and branching bisimulation, as well as a notion of $\varepsilon$-perturbed bisimulation that relat
Externí odkaz:
http://arxiv.org/abs/2407.07584