Zobrazeno 1 - 10
of 20 453
pro vyhledávání: '"supervised deep learning"'
Recent advances have revealed that the rate of convergence of the expected test error in deep supervised learning decays as a function of the intrinsic dimension and not the dimension $d$ of the input space. Existing literature defines this intrinsic
Externí odkaz:
http://arxiv.org/abs/2412.09779
Autor:
Gong, Hanying1,2 (AUTHOR) gonghy@stumail.nwu.edu.cn, Yu, Zehao1,2 (AUTHOR) yuzehao@stumail.nwu.edu.cn, Zhang, Shiqiang1,2 (AUTHOR) zhangsq@nwu.edu.cn, Zhou, Gang1,2 (AUTHOR)
Publikováno v:
Remote Sensing. Oct2024, Vol. 16 Issue 19, p3575. 19p.
Publikováno v:
Medical Imaging with Deep Learning, Jul 2024, Paris, France
Fully supervised deep models have shown promising performance for many medical segmentation tasks. Still, the deployment of these tools in clinics is limited by the very timeconsuming collection of manually expert-annotated data. Moreover, most of th
Externí odkaz:
http://arxiv.org/abs/2411.02466
Autor:
Saqib, Danyal, Hussain, Wajahat
Learning Based Robot Grasping currently involves the use of labeled data. This approach has two major disadvantages. Firstly, labeling data for grasp points and angles is a strenuous process, so the dataset remains limited. Secondly, human labeling i
Externí odkaz:
http://arxiv.org/abs/2410.14084
Deep learning methods - consisting of a class of deep neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method - are nowadays key tools to solve data driven supervised learning problems. Despite the great success of S
Externí odkaz:
http://arxiv.org/abs/2410.10533
Autor:
Aalbers, J., Abe, K., Adrover, M., Maouloud, S. Ahmed, Althueser, L., Amaral, D. W. P., Andrieu, B., Angelino, E., Martin, D. Antón, Antunovic, B., Aprile, E., Babicz, M., Bajpai, D., Balzer, M., Barberio, E., Baudis, L., Bazyk, M., Bell, N. F., Bellagamba, L., Biondi, R., Biondi, Y., Bismark, A., Boehm, C., Boese, K., Braun, R., Breskin, A., Brommer, S., Brown, A., Bruni, G., Budnik, R., Cai, C., Capelli, C., Chauvin, A., Chavez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Doerenkamp, M., Drexlin, G., Eitel, K., Elykov, A., Engel, R., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fujikawa, K., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Garroum, N., Giacomobono, R., Girard, F., Glade-Beucke, R., Glück, F., Grandi, L., Grigat, J., Größle, R., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Hannen, V., Hansmann-Menzemer, S., Hargittai, N., Higuera, A., Hils, C., Hiraoka, K., Hoetzsch, L., Hoferichter, M., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., James, R. S., Joerg, F., Kahlert, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Keller, M., Kharbanda, P., Kilminster, B., Kleifges, M., Klute, M., Kobayashi, M., Koke, D., Kopec, A., von Krosigk, B., Kuger, F., LaCascio, L., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, A., Li, S., Liang, S., Liang, Z., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Lucchetti, G. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Maier, B., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Menéndez, J., Messina, M., Milosovic, B., Milutinovic, S., Miuchi, K., Miyata, R., Molinario, A., Monteiro, C. M. B., Morå, K., Moriyama, S., Morteau, E., Mosbacher, Y., Müller, J., Murra, M., Newstead, J. L., Ni, K., O'Hare, C., Oberlack, U., Obradovic, M., Ostrowskiy, I., Ouahada, S., Paetsch, B., Pan, Y., Pandurovic, M., Pellegrini, Q., Peres, R., Piastra, F., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qiao, K., Qin, J., Rajado, M., García, D. Ramírez, Ravindran, A., Razeto, A., Sanchez, L., Sanchez-Lucas, P., Sartorelli, G., Scaffidi, A., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Schwenck, A., Schwenk, A., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Sharma, S., Shen, W., Shi, S. Y., Shimada, T., Simgen, H., Singh, R., Solmaz, M., Stanley, O., Steidl, M., Stevens, A., Takeda, A., Tan, P. -L., Thers, D., Thümmler, T., Tönnies, F., Toschi, F., Trinchero, G., Trotta, R., Tunnell, C. D., Urquijo, P., Utoyama, M., Valerius, K., Vecchi, S., Vetter, S., Volta, G., Vorkapic, D., Wang, W., Weerman, K. M., Weinheimer, C., Weiss, M., Wenz, D., Wilson, M., Wittweg, C., Wolf, J., Wu, V. H. S., Wüstling, S., Wurm, M., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., Zhong, M., Zuber, K.
We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-ton scale liquid Xenon-based direct detection experiment, DARWIN. We tr
Externí odkaz:
http://arxiv.org/abs/2410.00755
Autor:
Long, Jianyu1 (AUTHOR), Chen, Yibin1,2 (AUTHOR), Yang, Zhe1 (AUTHOR), Huang, Yunwei1 (AUTHOR), Li, Chuan1 (AUTHOR) chuanli@dgut.edu.cn
Publikováno v:
International Journal of Production Research. Dec2023, Vol. 61 Issue 23, p8238-8251. 14p.
Autor:
Karim, Mohammad Ehsanul1,2 (AUTHOR) ehsan.karim@ubc.ca
Publikováno v:
BMC Medical Research Methodology. 8/2/2024, Vol. 24 Issue 1, p1-10. 10p.
Autor:
Al-Hemyari, Emad1 (AUTHOR) emad.al-hemyari@postgrad.curtin.edu.au, Collet, Olivia1 (AUTHOR), Tertyshnikov, Konstantin1 (AUTHOR), Pevzner, Roman1 (AUTHOR) r.pevzner@curtin.edu.au
Publikováno v:
Sensors (14248220). Nov2024, Vol. 24 Issue 21, p6978. 15p.
The most prevalent form of bladder cancer is urothelial carcinoma, characterized by a high recurrence rate and substantial lifetime treatment costs for patients. Grading is a prime factor for patient risk stratification, although it suffers from inco
Externí odkaz:
http://arxiv.org/abs/2405.15275