Zobrazeno 1 - 10
of 23 794
pro vyhledávání: '"KOEHLER P."'
We consider the problem of sampling a multimodal distribution with a Markov chain given a small number of samples from the stationary measure. Although mixing can be arbitrarily slow, we show that if the Markov chain has a $k$th order spectral gap, i
Externí odkaz:
http://arxiv.org/abs/2411.09117
We introduce the Autoregressive PDE Emulator Benchmark (APEBench), a comprehensive benchmark suite to evaluate autoregressive neural emulators for solving partial differential equations. APEBench is based on JAX and provides a seamlessly integrated d
Externí odkaz:
http://arxiv.org/abs/2411.00180
In this contribution we extend the concept of a Petri net morphism to Elementary Object Systems (EOS). EOS are a nets-within-nets formalism, i.e. we allow the tokens of a Petri net to be Petri nets again. This nested structure has the consequence tha
Externí odkaz:
http://arxiv.org/abs/2411.00149
Autor:
Wald, Tassilo, Ulrich, Constantin, Köhler, Gregor, Zimmerer, David, Denner, Stefan, Baumgartner, Michael, Isensee, Fabian, Jaini, Priyank, Maier-Hein, Klaus H.
What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to
Externí odkaz:
http://arxiv.org/abs/2410.23107
Traditional panel data causal inference frameworks, such as difference-in-differences and synthetic control methods, rely on pre-intervention data to estimate counterfactuals, which may not be available in real-world settings when interventions are i
Externí odkaz:
http://arxiv.org/abs/2410.16391
Autor:
Aker, M., Batzler, D., Beglarian, A., Beisenkötter, J., Biassoni, M., Bieringer, B., Biondi, Y., Block, F., Bornschein, B., Bornschein, L., Böttcher, M., Carminati, M., Chatrabhuti, A., Chilingaryan, S., Daniel, B. A., Descher, M., Barrero, D. Díaz, Doe, P. J., Dragoun, O., Drexlin, G., Edzards, F., Eitel, K., Ellinger, E., Engel, R., Enomoto, S., Felden, A., Fengler, C., Fiorini, C., Formaggio, J. A., Forstner, C., Fränkle, F. M., Gagliardi, G., Gauda, K., Gavin, A. S., Gil, W., Glück, F., Grössle, R., Gutknecht, N., Hannen, V., Hasselmann, L., Helbing, K., Henke, H., Heyns, S., Hiller, R., Hillesheimer, D., Hinz, D., Höhn, T., Huber, A., Jansen, A., Khosonthongkee, K., Köhler, C., Köllenberger, L., Kopmann, A., Kovač, N., La Cascio, L., Lasserre, T., Lauer, J., Le, T. L., Lebeda, O., Lehnert, B., Li, G., Lokhov, A., Machatschek, M., Mark, M., Marsteller, A., McMichael, K., Melzer, C., Mertens, S., Mohanty, S., Mostafa, J., Müller, K., Nava, A., Neumann, H., Niemes, S., Onillon, A., Parno, D. S., Pavan, M., Pinsook, U., Poon, A. W. P., Poyato, J. M. L., Priester, F., Ráliš, J., Ramachandran, S., Robertson, R. G. H., Rodenbeck, C., Röllig, M., Sack, R., Saenz, A., Salomon, R., Schäfer, P., Schlösser, K., Schlösser, M., Schlüter, L., Schneidewind, S., Schrank, M., Schürmann, J., Schütz, A. K., Schwemmer, A., Schwenck, A., Seeyangnok, J., Šefčík, M., Siegmann, D., Simon, F., Songwadhana, J., Spanier, F., Spreng, D., Sreethawong, W., Steidl, M., Štorek, J., Stribl, X., Sturm, M., Suwonjandee, N., Jerome, N. Tan, Telle, H. H., Thorne, L. A., Thümmler, T., Titov, N., Tkachev, I., Urban, K., Valerius, K., Vénos, D., Weinheimer, C., Welte, S., Wendel, J., Wetter, M., Wiesinger, C., Wilkerson, J. F., Wolf, J., Wüstling, S., Wydra, J., Xu, W., Zadorozhny, S., Zeller, G.
The precision measurement of the tritium $\beta$-decay spectrum performed by the KATRIN experiment provides a unique way to search for general neutrino interactions (GNI). All theoretical allowed GNI terms involving neutrinos are incorporated into a
Externí odkaz:
http://arxiv.org/abs/2410.13895
Autor:
Thellmann, Klaudia, Stadler, Bernhard, Fromm, Michael, Buschhoff, Jasper Schulze, Jude, Alex, Barth, Fabio, Leveling, Johannes, Flores-Herr, Nicolas, Köhler, Joachim, Jäkel, René, Ali, Mehdi
The rise of Large Language Models (LLMs) has revolutionized natural language processing across numerous languages and tasks. However, evaluating LLM performance in a consistent and meaningful way across multiple European languages remains challenging
Externí odkaz:
http://arxiv.org/abs/2410.08928
Autor:
Brandizzi, Nicolo', Abdelwahab, Hammam, Bhowmick, Anirban, Helmer, Lennard, Stein, Benny Jörg, Denisov, Pavel, Saleem, Qasid, Fromm, Michael, Ali, Mehdi, Rutmann, Richard, Naderi, Farzad, Agy, Mohamad Saif, Schwirjow, Alexander, Küch, Fabian, Hahn, Luzian, Ostendorff, Malte, Suarez, Pedro Ortiz, Rehm, Georg, Wegener, Dennis, Flores-Herr, Nicolas, Köhler, Joachim, Leveling, Johannes
This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performance multilingual large language models (LLMs). The project goal is to
Externí odkaz:
http://arxiv.org/abs/2410.08800
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
Autor:
Nahass, George R., Koehler, Emma, Tomaras, Nicholas, Lopez, Danny, Cheung, Madison, Palacios, Alexander, Peterson, Jefferey, Hubschman, Sasha, Green, Kelsey, Purnell, Chad A., Setabutr, Pete, Tran, Ann Q., Yi, Darvin
Periorbital segmentation and distance prediction using deep learning allows for the objective quantification of disease state, treatment monitoring, and remote medicine. However, there are currently no reports of segmentation datasets for the purpose
Externí odkaz:
http://arxiv.org/abs/2409.20407