Zobrazeno 1 - 10
of 4 274
pro vyhledávání: '"Angel, Lopez"'
Autor:
Richard, Johan, Giroud, Rémi, Laurent, Florence, Krajnović, Davor, Jeanneau, Alexandre, Bacon, Roland, Abreu, Manuel, Adamo, Angela, Araujo, Ricardo, Bouché, Nicolas, Brinchmann, Jarle, Cai, Zhemin, Castro, Norberto, Calcines, Ariadna, Chapuis, Diane, Claeyssens, Adélaïde, Cortese, Luca, Daddi, Emanuele, Davison, Christopher, Goodwin, Michael, Harris, Robert, Hayes, Matthew, Jauzac, Mathilde, Kelz, Andreas, Kneib, Jean-Paul, Lanotte, Audrey A., Lawrence, Jon, Bouteiller, Vianney Le, Breton, Rémy Le, Lehnert, Matthew, Sanchez, Angel Lopez, McGregor, Helen, McLeod, Anna F., Monteiro, Manuel, Morris, Simon, Opitom, Cyrielle, Pécontal, Arlette, Robertson, David, Roth, Martin M., van de Sande, Jesse, Smith, Russell, Steinmetz, Matthias, Swinbank, Mark, Urrutia, Tanya, Verhamme, Anne, Weilbacher, Peter M., Wendt, Martin, Wildi, François, Zheng, Jessica, consortium, The BlueMUSE
BlueMUSE is a blue-optimised, medium spectral resolution, panoramic integral field spectrograph under development for the Very Large Telescope (VLT). With an optimised transmission down to 350 nm, spectral resolution of R$\sim$3500 on average across
Externí odkaz:
http://arxiv.org/abs/2406.13914
Akademický článek
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Akademický článek
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Autor:
Otsuka, Hikari, Chijiwa, Daiki, García-Arias, Ángel López, Okoshi, Yasuyuki, Kawamura, Kazushi, Van Chu, Thiem, Fujiki, Daichi, Takeuchi, Susumu, Motomura, Masato
Randomly initialized dense networks contain subnetworks that achieve high accuracy without weight learning -- strong lottery tickets (SLTs). Recently, Gadhikar et al. (2023) demonstrated that SLTs can also be found within a randomly pruned source net
Externí odkaz:
http://arxiv.org/abs/2402.14029
Autor:
Yan, Jiale, Ito, Hiroaki, García-Arias, Ángel López, Okoshi, Yasuyuki, Otsuka, Hikari, Kawamura, Kazushi, Van Chu, Thiem, Motomura, Masato
Publikováno v:
Proceedings of the Second Learning on Graphs Conference (LoG 2023), PMLR 231
The Strong Lottery Ticket Hypothesis (SLTH) demonstrates the existence of high-performing subnetworks within a randomly initialized model, discoverable through pruning a convolutional neural network (CNN) without any weight training. A recent study,
Externí odkaz:
http://arxiv.org/abs/2312.03236
In this paper, a novel method to perform model-based clustering of time series is proposed. The procedure relies on two iterative steps: (i) K global forecasting models are fitted via pooling by considering the series pertaining to each cluster and (
Externí odkaz:
http://arxiv.org/abs/2305.00473
Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with real-valued time series, categorical time series have received much less attention. However, the development of data mining techniques for this kind of d
Externí odkaz:
http://arxiv.org/abs/2304.12332
The 21st century has witnessed a growing interest in the analysis of time series data. Whereas most of the literature on the topic deals with real-valued time series, ordinal time series have typically received much less attention. However, the devel
Externí odkaz:
http://arxiv.org/abs/2304.12251
Time series clustering is a central machine learning task with applications in many fields. While the majority of the methods focus on real-valued time series, very few works consider series with discrete response. In this paper, the problem of clust
Externí odkaz:
http://arxiv.org/abs/2304.12249
Autor:
Omer Farooq, Maida Shahid, Shazia Arshad, Ayesha Altaf, Faiza Iqbal, Yini Airet Miro Vera, Miguel Angel Lopez Flores, Imran Ashraf
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely re
Externí odkaz:
https://doaj.org/article/a2ffe7dc33774070a9eec966a13b7d0a
