Zobrazeno 1 - 10
of 1 037 552
pro vyhledávání: '"van der Have, A."'
Autor:
Maarleveld, R., Veeger, H. E. J., van der Helm, F. C. T., Son, J., Lieber, R. L., van der Kruk, E.
Musculoskeletal (MSK) models offer a non-invasive way to understand biomechanical loads on joints and tendons, which are difficult to measure directly. Variations in muscle strength, especially relative differences between muscles, significantly impa
Externí odkaz:
http://arxiv.org/abs/2411.00071
Symmetries have proven useful in machine learning models, improving generalisation and overall performance. At the same time, recent advancements in learning dynamical systems rely on modelling the underlying Hamiltonian to guarantee the conservation
Externí odkaz:
http://arxiv.org/abs/2410.08087
Given a mild solution $X$ to a semilinear stochastic partial differential equation (SPDE), we consider an exponential change of measure based on its infinitesimal generator $L$, defined in the topology of bounded pointwise convergence. The changed me
Externí odkaz:
http://arxiv.org/abs/2409.08057
Autor:
Vaccaro, Davide, van der Kuur, Jan, van der Hulst, Paul, Vos, Tobias, de Wit, Martin, Gottardi, Luciano, Ravensberg, Kevin, Taralli, Emanuele, Adams, Joseph, Bandler, Simon, Bennet, Douglas, Chervenak, James, Doriese, Bertrand, Durkin, Malcolm, Gard, Johnathon, Reintsema, Carl, Sakai, Kazuhiro, Smith, Steven, Ullom, Joel, Wakeham, Nicholas, Herder, Jan-Willem den, jackson, Brian, Khosropanah, Pourya, Gao, Jian-Rong, Roelfsema, Peter, Simionescu, Aurora
The X-ray Integral Field Unit (X-IFU) is an instrument of ESA's future NewAthena space observatory, with the goal to provide high-energy resolution ($<$ 4 eV at X-ray energies up to 7 keV) and high-spatial resolution (9") spectroscopic imaging over t
Externí odkaz:
http://arxiv.org/abs/2409.05643
Deep learning sometimes appears to work in unexpected ways. In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network consisting of a sequence of first-orde
Externí odkaz:
http://arxiv.org/abs/2411.00247
Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process (DGP). Existing approaches to addressing shifts, such as concept drift adaptation, are limited by
Externí odkaz:
http://arxiv.org/abs/2411.00186
Schema matching -- the task of finding matches between attributes across disparate data sources with different tables and hierarchies -- is critical for creating interoperable machine learning (ML)-ready data. Addressing this fundamental data-centric
Externí odkaz:
http://arxiv.org/abs/2410.24105
We experimentally investigate on-chip control and analysis of spatially multimode nonlinear interactions in silicon nitride waveguide circuits. Using widely different dispersion of transverse supermodes in a strongly coupled dual-core waveguide secti
Externí odkaz:
http://arxiv.org/abs/2410.24073
The predominant de facto paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on different subgroups. However, such data-only testing methods operate under the
Externí odkaz:
http://arxiv.org/abs/2410.24005
Digital Twins (DTs) are computational models that simulate the states and temporal dynamics of real-world systems, playing a crucial role in prediction, understanding, and decision-making across diverse domains. However, existing approaches to DTs of
Externí odkaz:
http://arxiv.org/abs/2410.23691