Zobrazeno 1 - 10
of 6 996
pro vyhledávání: '"Korhonen, P. A."'
Autor:
Bentert, Matthias, Fomin, Fedor V., Golovach, Petr A., Korhonen, Tuukka, Lochet, William, Panolan, Fahad, Ramanujan, M. S., Saurabh, Saket, Simonov, Kirill
Cycle packing is a fundamental problem in optimization, graph theory, and algorithms. Motivated by recent advancements in finding vertex-disjoint paths between a specified set of vertices that either minimize the total length of the paths [Bj\"orklun
Externí odkaz:
http://arxiv.org/abs/2410.18878
In the Hedge Cut problem, the edges of a graph are partitioned into groups called hedges, and the question is what is the minimum number of hedges to delete to disconnect the graph. Ghaffari, Karger, and Panigrahi [SODA 2017] showed that Hedge Cut ca
Externí odkaz:
http://arxiv.org/abs/2410.17641
The properties of functional brain networks are heavily influenced by how the network nodes are defined. A common approach uses Regions of Interest (ROIs), which are predetermined collections of fMRI measurement voxels, as network nodes. Their defini
Externí odkaz:
http://arxiv.org/abs/2410.05972
Autor:
Korhonen, Mikko
Let $G$ be the finite simple group of Lie type $G = E_7(q)$, where $q$ is an odd prime power. Then $G$ is an index $2$ subgroup of the adjoint group $G_{\operatorname{ad}}$, which is also denoted by $G_{\operatorname{ad}} = \operatorname{Inndiag}(G)$
Externí odkaz:
http://arxiv.org/abs/2409.20281
Autor:
Köksal, Abdullatif, Thaler, Marion, Imani, Ayyoob, Üstün, Ahmet, Korhonen, Anna, Schütze, Hinrich
Instruction tuning enhances large language models (LLMs) by aligning them with human preferences across diverse tasks. Traditional approaches to create instruction tuning datasets face serious challenges for low-resource languages due to their depend
Externí odkaz:
http://arxiv.org/abs/2409.12958
Autor:
Korhonen, Keijo, Vappula, Hetta, Glos, Adam, Cattaneo, Marco, Zimborás, Zoltán, Borrelli, Elsi-Mari, Rossi, Matteo A. C., García-Pérez, Guillermo, Cavalcanti, Daniel
Achieving high-precision measurements on near-term quantum devices is critical for advancing quantum computing applications. In this paper, we explore several practical techniques to enhance measurement accuracy using randomized measurements, focusin
Externí odkaz:
http://arxiv.org/abs/2409.02575
Autor:
Zhao, Raoyuan, Köksal, Abdullatif, Liu, Yihong, Weissweiler, Leonie, Korhonen, Anna, Schütze, Hinrich
Traditional benchmarking in NLP typically involves using static held-out test sets. However, this approach often results in an overestimation of performance and lacks the ability to offer comprehensive, interpretable, and dynamic assessments of NLP m
Externí odkaz:
http://arxiv.org/abs/2408.17437
Autor:
Fytas, Panagiotis, Breger, Anna, Selby, Ian, Baker, Simon, Shahipasand, Shahab, Korhonen, Anna
Developing imaging models capable of detecting pathologies from chest X-rays can be cost and time-prohibitive for large datasets as it requires supervision to attain state-of-the-art performance. Instead, labels extracted from radiology reports may s
Externí odkaz:
http://arxiv.org/abs/2408.04121
We propose an application of online hard sample mining for efficient training of Neural Radiance Fields (NeRF). NeRF models produce state-of-the-art quality for many 3D reconstruction and rendering tasks but require substantial computational resource
Externí odkaz:
http://arxiv.org/abs/2408.03193
Autor:
Marconi, A., Abreu, M., Adibekyan, V., Alberti, V., Albrecht, S., Alcaniz, J., Aliverti, M., Prieto, C. Allende, Gómez, J. D. Alvarado, Alves, C. S., Amado, P. J., Amate, M., Andersen, M. I., Antoniucci, S., Artigau, E., Bailet, C., Baker, C., Baldini, V., Balestra, A., Barnes, S. A., Baron, F., Barros, S. C. C., Bauer, S. M., Beaulieu, M., Bellido-Tirado, O., Benneke, B., Bensby, T., Bergin, E. A., Berio, P., Biazzo, K., Bigot, L., Bik, A., Birkby, J. L., Blind, N., Boebion, O., Boisse, I., Bolmont, E., Bolton, J. S., Bonaglia, M., Bonfils, X., Bonhomme, L., Borsa, F., Bouret, J. -C., Brandeker, A., Brandner, W., Broeg, C. H., Brogi, M., Brousseau, D., Brucalassi, A., Brynnel, J., Buchhave, L. A., Buscher, D. F., Cabona, L., Cabral, A., Calderone, G., Calvo-Ortega, R., Cantalloube, F., Martins, B. L. Canto, Carbonaro, L., Caujolle, Y., Chauvin, G., Chazelas, B., Cheffot, A. -L., Cheng, Y. S., Chiavassa, A., Christensen, L., Cirami, R., Cirasuolo, M., Cook, N. J., Cooke, R. J., Coretti, I., Covino, S., Cowan, N., Cresci, G., Cristiani, S., Parro, V. Cunha, Cupani, G., D'Odorico, V., Dadi, K., Leão, I. de Castro, De Cia, A., De Medeiros, J. R., Debras, F., Debus, M., Delorme, A., Demangeon, O., Derie, F., Dessauges-Zavadsky, M., Di Marcantonio, P., Di Stefano, S., Dionies, F., de Souza, A. Domiciano, Doyon, R., Dunn, J., Egner, S., Ehrenreich, D., Faria, J. P., Ferruzzi, D., Feruglio, C., Fisher, M., Fontana, A., Frank, B. S., Fuesslein, C., Fumagalli, M., Fusco, T., Fynbo, J., Gabella, O., Gaessler, W., Gallo, E., Gao, X., Genolet, L., Genoni, M., Giacobbe, P., Giro, E., Goncalves, R. S., Gonzalez, O. A., Hernández, J. I. González, Gouvret, C., Temich, F. Gracia, Haehnelt, M. G., Haniff, C., Hatzes, A., Helled, R., Hoeijmakers, H. J., Hughes, I., Huke, P., Ivanisenko, Y., Järvinen, A. S., Järvinen, S. P., Kaminski, A., Kern, J., Knoche, J., Kordt, A., Korhonen, H., Korn, A. J., Kouach, D., Kowzan, G., Kreidberg, L., Landoni, M., Lanotte, A. A., Lavail, A., Lavie, B., Lee, D., Lehmitz, M., Li, J., Li, W., Liske, J., Lovis, C., Lucatello, S., Lunney, D., MacIntosh, M. J., Madhusudhan, N., Magrini, L., Maiolino, R., Maldonado, J., Malo, L., Man, A. W. S., Marquart, T., Marques, C. M. J., Marques, E. L., Martinez, P., Martins, A., Martins, C. J. A. P., Martins, J. H. C., Maslowski, P., Mason, C. A., Mason, E., McCracken, R. A., Sousa, M. A. F. Melo e, Mergo, P., Micela, G., Milaković, D., Molliere, P., Monteiro, M. A., Montgomery, D., Mordasini, C., Morin, J., Mucciarelli, A., Murphy, M. T., N'Diaye, M., Nardetto, N., Neichel, B., Neri, N., Niedzielski, A. T., Niemczura, E., Nisini, B., Nortmann, L., Noterdaeme, P., Nunes, N. J., Oggioni, L., Olchewsky, F., Oliva, E., Onel, H., Origlia, L., Ostlin, G., Ouellette, N. N. -Q., Palle, E., Papaderos, P., Pariani, G., Pasquini, L., Castro, J. Peñate, Pepe, F., Peroux, C., Levasseur, L. Perreault, Perruchot, S., Petit, P., Pfuhl, O., Pino, L., Piqueras, J., Piskunov, N., Pollo, A., Poppenhaeger, K., Porru, M., Puschnig, J., Quirrenbach, A., Rauscher, E., Rebolo, R., Redaelli, E. M. A., Reffert, S., Reid, D. T., Reiners, A., Richter, P., Riva, M., Rivoire, S., Rodriguez-López, C., Roederer, I. U., Romano, D., Roth, M., Rousseau, S., Rowe, J., Saccardi, A., Salvadori, S., Sanna, N., Santos, N. C., Diaz, P. Santos, Sanz-Forcada, J., Sarajlic, M., Sauvage, J. -F., Savio, D., Scaudo, A, Schäfer, S., Schiavon, R. P., Schmidt, T. M., Selmi, C., Simoes, R., Simonnin, A., Sivanandam, S., Sordet, M., Sordo, R., Sortino, F., Sosnowska, D., Sousa, S. G., Spang, A., Spiga, R., Stempels, E., Stevenson, J. R. Y., Strassmeier, K. G., Mascareño, A. Suárez, Sulich, A., Sun, X., Tanvir, N. R., Tenegi-Sangines, F., Thibault, S., Thompson, S. J., Tisserand, P., Tozzi, A., Turbet, M., Veran, J. -P., Vallee, P., Vanni, I., Varas, R., Vega-Moreno, A., Venn, K. A., Verma, A., Vernet, J., Viel, M., Wade, G., Waring, C., Weber, M., Weder, J., Wehbe, B., Weingrill, J., Woche, M., Xompero, M., Zackrisson, E., Zanutta, A., Osorio, M. R. Zapatero, Zechmeister, M., Zimara, J.
The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs
Externí odkaz:
http://arxiv.org/abs/2407.14601