Zobrazeno 1 - 10
of 74 388
pro vyhledávání: '"A. Arno"'
The covariance for clean data given a noisy observation is an important quantity in many conditional generation methods for diffusion models. Current methods require heavy test-time computation, altering the standard diffusion training process or den
Externí odkaz:
http://arxiv.org/abs/2410.11149
The origins of the magnetic fields that power gamma-ray burst (GRB) afterglow emission are not fully understood. One possible channel for generating these fields involves the pre-conditioning of the circumburst medium: in the early afterglow phase, p
Externí odkaz:
http://arxiv.org/abs/2410.05388
Autor:
Ali, Mehdi, Fromm, Michael, Thellmann, Klaudia, Ebert, Jan, Weber, Alexander Arno, Rutmann, Richard, Jain, Charvi, Lübbering, Max, Steinigen, Daniel, Leveling, Johannes, Klug, Katrin, Buschhoff, Jasper Schulze, Jurkschat, Lena, Abdelwahab, Hammam, Stein, Benny Jörg, Sylla, Karl-Heinz, Denisov, Pavel, Brandizzi, Nicolo', Saleem, Qasid, Bhowmick, Anirban, Helmer, Lennard, John, Chelsea, Suarez, Pedro Ortiz, Ostendorff, Malte, Jude, Alex, Manjunath, Lalith, Weinbach, Samuel, Penke, Carolin, Filatov, Oleg, Asaadi, Shima, Barth, Fabio, Sifa, Rafet, Küch, Fabian, Herten, Andreas, Jäkel, René, Rehm, Georg, Kesselheim, Stefan, Köhler, Joachim, Flores-Herr, Nicolas
We present two multilingual LLMs designed to embrace Europe's linguistic diversity by supporting all 24 official languages of the European Union. Trained on a dataset comprising around 60% non-English data and utilizing a custom multilingual tokenize
Externí odkaz:
http://arxiv.org/abs/2410.03730
One of the central goals of causal machine learning is the accurate estimation of heterogeneous treatment effects from observational data. In recent years, meta-learning has emerged as a flexible, model-agnostic paradigm for estimating conditional av
Externí odkaz:
http://arxiv.org/abs/2409.15503
Differential equations are important mechanistic models that are integral to many scientific and engineering applications. With the abundance of available data there has been a growing interest in data-driven physics-informed models. Gaussian process
Externí odkaz:
http://arxiv.org/abs/2409.13876
Autor:
Grimm, Thomas W., Hoefnagels, Arno
A powerful approach to computing Feynman integrals or cosmological correlators is to consider them as solution to systems of differential equations. Often these can be chosen to be Gelfand-Kapranov-Zelevinsky (GKZ) systems. However, their naive const
Externí odkaz:
http://arxiv.org/abs/2409.13815
Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of high-dimensional features from neighboring vertices w
Externí odkaz:
http://arxiv.org/abs/2409.11129
We present the SynSUM benchmark, a synthetic dataset linking unstructured clinical notes to structured background variables. The dataset consists of 10,000 artificial patient records containing tabular variables (like symptoms, diagnoses and underlyi
Externí odkaz:
http://arxiv.org/abs/2409.08936
The process of 3D scene reconstruction can be affected by numerous uncertainty sources in real-world scenes. While Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (GS) achieve high-fidelity rendering, they lack built-in mechanisms to directl
Externí odkaz:
http://arxiv.org/abs/2409.06407
Autor:
Trigg, Aaron C., Stewart, Rachel, van Kooten, Alex, Burns, Eric, Roberts, Oliver J., Frederiks, Dmitry D., Baring, Matthew G., Younes, George, Svinkin, Dmitry S., Wadiasingh, Zorawar, Veres, Peter, Bhat, Narayana, Briggs, Michael S., Scotton, Lorenzo, Goldstein, Adam, Busmann, Malte, O'Connor, Brendan, Hu, Lei, Gruen, Daniel, Riffeser, Arno, Zoeller, Raphael, Palmese, Antonella, Huppenkothen, Daniela, Kouveliotou, Chryssa
We present the detection and analysis of GRB 231115A, a candidate extragalactic magnetar giant flare (MGF) observed by Fermi/GBM and localized by INTEGRAL to the starburst galaxy M82. This burst exhibits distinctive temporal and spectral characterist
Externí odkaz:
http://arxiv.org/abs/2409.06056