Zobrazeno 1 - 10
of 23 383
pro vyhledávání: '"smith, michael"'
Autor:
The Multimodal Universe Collaboration, Audenaert, Jeroen, Bowles, Micah, Boyd, Benjamin M., Chemaly, David, Cherinka, Brian, Ciucă, Ioana, Cranmer, Miles, Do, Aaron, Grayling, Matthew, Hayes, Erin E., Hehir, Tom, Ho, Shirley, Huertas-Company, Marc, Iyer, Kartheik G., Jablonska, Maja, Lanusse, Francois, Leung, Henry W., Mandel, Kaisey, Martínez-Galarza, Juan Rafael, Melchior, Peter, Meyer, Lucas, Parker, Liam H., Qu, Helen, Shen, Jeff, Smith, Michael J., Stone, Connor, Walmsley, Mike, Wu, John F.
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of astronomical observ
Externí odkaz:
http://arxiv.org/abs/2412.02527
Autor:
Moya-Sánchez, E. Ulises, Mikail, Yazid S., Nyang'anyi, Daisy, Smith, Michael J., Smythe, Isabella
Machine learning has great potential to increase crop production and resilience to climate change. Accurate maps of where crops are grown are a key input to a number of downstream policy and research applications. In this proposal, we present prelimi
Externí odkaz:
http://arxiv.org/abs/2411.02627
Autor:
Vu, Tuan-Hung, Valle, Eduardo, Bursuc, Andrei, Kerssies, Tommie, de Geus, Daan, Dubbelman, Gijs, Qian, Long, Zhu, Bingke, Chen, Yingying, Tang, Ming, Wang, Jinqiao, Vojíř, Tomáš, Šochman, Jan, Matas, Jiří, Smith, Michael, Ferrie, Frank, Basu, Shamik, Sakaridis, Christos, Van Gool, Luc
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. We define two categories of reliability: (1) semantic reliability, whic
Externí odkaz:
http://arxiv.org/abs/2409.15107
Light scrambling and focal ratio degradation of thin multimode fibers with different core geometries
Autor:
Lee, Man-Yin Leo, Lin, Zhiheng, Hui, Chit-Ho, Yan, Renbin, Cheung, YiuHung, Hung, Horace Tsz-Hong, Bershady, Matthew A., Chattopadhyay, Sabysachi, Smith, Michael P.
The performance of fiber-fed astronomical spectrographs is highly influenced by the properties of fibers. The near-field and far-field scrambling characteristics have a profound impact on the line spread function (LSF) of the spectra. Focal ratio deg
Externí odkaz:
http://arxiv.org/abs/2408.07961
Autor:
Iyer, Kartheik G., Yunus, Mikaeel, O'Neill, Charles, Ye, Christine, Hyk, Alina, McCormick, Kiera, Ciuca, Ioana, Wu, John F., Accomazzi, Alberto, Astarita, Simone, Chakrabarty, Rishabh, Cranney, Jesse, Field, Anjalie, Ghosal, Tirthankar, Ginolfi, Michele, Huertas-Company, Marc, Jablonska, Maja, Kruk, Sandor, Liu, Huiling, Marchidan, Gabriel, Mistry, Rohit, Naiman, J. P., Peek, J. E. G., Polimera, Mugdha, Rodriguez, Sergio J., Schawinski, Kevin, Sharma, Sanjib, Smith, Michael J., Ting, Yuan-Sen, Walmsley, Mike
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable lite
Externí odkaz:
http://arxiv.org/abs/2408.01556
This work presents AstroPT, an autoregressive pretrained transformer developed with astronomical use-cases in mind. The AstroPT models presented here have been pretrained on 8.6 million $512 \times 512$ pixel $grz$-band galaxy postage stamp observati
Externí odkaz:
http://arxiv.org/abs/2405.14930
