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pro vyhledávání: '"Smith, Michael J."'
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:
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
Autor:
Perkowski, Ernest, Pan, Rui, Nguyen, Tuan Dung, Ting, Yuan-Sen, Kruk, Sandor, Zhang, Tong, O'Neill, Charlie, Jablonska, Maja, Sun, Zechang, Smith, Michael J., Liu, Huiling, Schawinski, Kevin, Iyer, Kartheik, UniverseTBD, Ioana Ciucă for
We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training. By employing a compact 7B-parameter LLaMA-2 model and focusing exclusively on a curated set of astronomy corpora -
Externí odkaz:
http://arxiv.org/abs/2401.01916
Autor:
Lourenço, Ricardo Barros, Smith, Michael J., Smullin, Sylvia, Jain, Umangi, Gonsamo, Alemu, Ouaknine, Arthur
Forests of the Earth are a vital carbon sink while providing an essential habitat for biodiversity. Vegetation productivity (VP) is a critical indicator of carbon uptake in the atmosphere. The leaf area index is a crucial vegetation index used in VP
Externí odkaz:
http://arxiv.org/abs/2310.18703
We introduce EarthPT -- an Earth Observation (EO) pretrained transformer. EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in min
Externí odkaz:
http://arxiv.org/abs/2309.07207