Zobrazeno 1 - 10
of 121 045
pro vyhledávání: '"But, Mihaela"'
Autor:
Lazanu, Ionel, Parvu, Mihaela
Publikováno v:
Symmetry 2024, 16(7), 869
The axion anti-quark nugget (A$\bar{\mathrm{Q}}$N) model was developed to explain in a natural way the asymmetry between matter and antimatter in Universe. In this hypothesis, a similitude between the dark and the visible components exists. The lack
Externí odkaz:
http://arxiv.org/abs/2407.04330
Autor:
Taylor-LaPole, Alyssa M., Paun, L. Mihaela, Lior, Dan, Weigand, Justin D, Puelz, Charles, Olufsen, Mette S.
Hypoplastic left heart syndrome (HLHS) is a congenital heart disease responsible for 23% of infant cardiac deaths each year. HLHS patients are born with an underdeveloped left heart, requiring several surgeries to reconstruct the aorta and create a s
Externí odkaz:
http://arxiv.org/abs/2406.18490
Autor:
Mirea, Anca G., Vlaicu, Ioana D., Derbali, Sarah, Neatu, Florentina, Tomulescu, Andrei G., Besleaga, Cristina, Enculescu, Monica, Kuncser, Andrei C., Iacoban, Alexandra C., Filipoiu, Nicolae, Cuzminschi, Marina, Nemnes, George A., Manolescu, Andrei, Florea, Mihaela, Pintilie, Ioana
Herein we present a comparative study among different mesoporous electron transporter layers (ETLs), namely nanometric m-TiO2, m-SnO2 and m-SnO2 quantum dots (QDs), deposited by spray coating method. The experimental data correlated with the photovol
Externí odkaz:
http://arxiv.org/abs/2406.18261
Tabular data is one of the most ubiquitous modalities, yet the literature on tabular generative foundation models is lagging far behind its text and vision counterparts. Creating such a model is hard, due to the heterogeneous feature spaces of differ
Externí odkaz:
http://arxiv.org/abs/2406.17673
Pseudo-labeling is a popular semi-supervised learning technique to leverage unlabeled data when labeled samples are scarce. The generation and selection of pseudo-labels heavily rely on labeled data. Existing approaches implicitly assume that the lab
Externí odkaz:
http://arxiv.org/abs/2406.13733
Autor:
Lu, Chris, Holt, Samuel, Fanconi, Claudio, Chan, Alex J., Foerster, Jakob, van der Schaar, Mihaela, Lange, Robert Tjarko
Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization is approached as an offline supervised learning task using manually-crafted convex los
Externí odkaz:
http://arxiv.org/abs/2406.08414
Constructing valid prediction intervals rather than point estimates is a well-established approach for uncertainty quantification in the regression setting. Models equipped with this capacity output an interval of values in which the ground truth tar
Externí odkaz:
http://arxiv.org/abs/2406.03258
In this work we prove global well-posedness for the massive Maxwell-Dirac system in the Lorenz gauge in $\mathbb{R}^{1+3}$, for small, sufficiently smooth and decaying initial data, as well as modified scattering for the solutions. Heuristically we e
Externí odkaz:
http://arxiv.org/abs/2406.02460
Estimating the conditional average treatment effect (CATE) from observational data is relevant for many applications such as personalized medicine. Here, we focus on the widespread setting where the observational data come from multiple environments,
Externí odkaz:
http://arxiv.org/abs/2406.02464
Autor:
Sun, Hao, van der Schaar, Mihaela
Aligning Large Language Models (LLMs) is crucial for enhancing their safety and utility. However, existing methods, primarily based on preference datasets, face challenges such as noisy labels, high annotation costs, and privacy concerns. In this wor
Externí odkaz:
http://arxiv.org/abs/2405.15624