Zobrazeno 1 - 10
of 314 369
pro vyhledávání: '"A., Kyle"'
Autor:
John, Peter St., Lin, Dejun, Binder, Polina, Greaves, Malcolm, Shah, Vega, John, John St., Lange, Adrian, Hsu, Patrick, Illango, Rajesh, Ramanathan, Arvind, Anandkumar, Anima, Brookes, David H, Busia, Akosua, Mahajan, Abhishaike, Malina, Stephen, Prasad, Neha, Sinai, Sam, Edwards, Lindsay, Gaudelet, Thomas, Regep, Cristian, Steinegger, Martin, Rost, Burkhard, Brace, Alexander, Hippe, Kyle, Naef, Luca, Kamata, Keisuke, Armstrong, George, Boyd, Kevin, Cao, Zhonglin, Chou, Han-Yi, Chu, Simon, Costa, Allan dos Santos, Darabi, Sajad, Dawson, Eric, Didi, Kieran, Fu, Cong, Geiger, Mario, Gill, Michelle, Hsu, Darren, Kaushik, Gagan, Korshunova, Maria, Kothen-Hill, Steven, Lee, Youhan, Liu, Meng, Livne, Micha, McClure, Zachary, Mitchell, Jonathan, Moradzadeh, Alireza, Mosafi, Ohad, Nashed, Youssef, Paliwal, Saee, Peng, Yuxing, Rabhi, Sara, Ramezanghorbani, Farhad, Reidenbach, Danny, Ricketts, Camir, Roland, Brian, Shah, Kushal, Shimko, Tyler, Sirelkhatim, Hassan, Srinivasan, Savitha, Stern, Abraham C, Toczydlowska, Dorota, Veccham, Srimukh Prasad, Venanzi, Niccolò Alberto Elia, Vorontsov, Anton, Wilber, Jared, Wilkinson, Isabel, Wong, Wei Jing, Xue, Eva, Ye, Cory, Yu, Xin, Zhang, Yang, Zhou, Guoqing, Zandstein, Becca, Dallago, Christian, Trentini, Bruno, Kucukbenli, Emine, Rvachov, Timur, Calleja, Eddie, Israeli, Johnny, Clifford, Harry, Haukioja, Risto, Haemel, Nicholas, Tretina, Kyle, Tadimeti, Neha, Costa, Anthony B
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language mode
Externí odkaz:
http://arxiv.org/abs/2411.10548
Autor:
Khan, Arham, Underwood, Robert, Siebenschuh, Carlo, Babuji, Yadu, Ajith, Aswathy, Hippe, Kyle, Gokdemir, Ozan, Brace, Alexander, Chard, Kyle, Foster, Ian
Deduplication is a major focus for assembling and curating training datasets for large language models (LLM) -- detecting and eliminating additional instances of the same content -- in large collections of technical documents. Unrestrained, duplicate
Externí odkaz:
http://arxiv.org/abs/2411.04257
Autor:
Brown, B. Alex, Gade, Alexandra, Stroberg, S. Ragnar, Escher, Jutta, Fossez, Kevin, Giuliani, Pablo, Hoffman, Calem R., Nazarewicz, Witold, Seng, Chien-Yeah, Sorensen, Agnieszka, Vassh, Nicole, Bazin, Daniel, Brown, Kyle W., Capri, Mark A., Crawford, Heather, Danielewic, Pawel, Drischler, Christian, Ruiz, Ronald F. Garcia, Godbey, Kyle, Grzywacz, Robert, Hlophe, Linda, Holt, Jeremy W., Iwasaki, Hiro, Lee, Dean, Lenzi, Silvia M., Liddick, Sean, Lubna, Rebeka, Macchiavelli, Augusto O., Pinedo, Gabriel Martinez, McCoy, Anna, Mercenne, Alexis, Minamisono, Kei, Monteagudo, Belen, Navratil, Petr, Ringle, Ryan, Sargsyan, Grigor, Schatz, Hendrik, Spieker, Mark-Christoph, Volya, Alexander, Zegers, Remco G. T., Zelevinsky, Vladimir, Zhang, Xilin
This white paper is the result of a collaboration by those that attended a workshop at the Facility for Rare Isotope Beams (FRIB), organized by the FRIB Theory Alliance (FRIB-TA), on Theoretical Justifications and Motivations for Early High-Profile F
Externí odkaz:
http://arxiv.org/abs/2410.06144
The rapid advancement of artificial intelligence (AI) technologies presents profound challenges to societal safety. As AI systems become more capable, accessible, and integrated into critical services, the dual nature of their potential is increasing
Externí odkaz:
http://arxiv.org/abs/2412.04029
The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervised few-shot learning within a much broader spectrum of meta-learned in
Externí odkaz:
http://arxiv.org/abs/2412.03782
Autor:
Spyrou, A., Richman, D., Couture, A., Fields, C. E., Liddick, S. N., Childers, K., Crider, B. P., DeYoung, P. A., Dombos, A. C., Gastis, P., Guttormsen, M., Hermansen, K., Larsen, A. C., Lewis, R., Lyons, S., Midtbø, J. E., Mosby, S., Muecher, D., Naqvi, F., Palmisano-Kyle, A., Perdikakis, G., Prokop, C., Schatz, H., Smith, M. K., Sumithrarachchi, C., Sweet, A.
Publikováno v:
Nature Communications ( 2024) 15:9608
Massive stars are a major source of chemical elements in the cosmos, ejecting freshly produced nuclei through winds and core-collapse supernova explosions into the interstellar medium. Among the material ejected, long lived radioisotopes, such as 60F
Externí odkaz:
http://arxiv.org/abs/2412.01723
Autor:
Lidbetter, Thomas, Lin, Kyle
This paper presents a booby trap game played between a defender and an attacker on a search space, which may be a compact subset of Euclidean space or a network. The defender has several booby traps and chooses where to plant them. The attacker, awar
Externí odkaz:
http://arxiv.org/abs/2412.01688
Autor:
Chakravarthy, Anirudh S, Zheng, Shuai Kyle, Huang, Xin, Hemachandra, Sachithra, Zhang, Xiao, Chai, Yuning, Chen, Zhao
Fine-tuning pre-trained models has become invaluable in computer vision and robotics. Recent fine-tuning approaches focus on improving efficiency rather than accuracy by using a mixture of smaller learning rates or frozen backbones. To return the spo
Externí odkaz:
http://arxiv.org/abs/2412.01930
This paper investigates the impact of sampling and pretraining using datasets with different image characteristics on the performance of self-supervised learning (SSL) models for object classification. To do this, we sample two apartment datasets fro
Externí odkaz:
http://arxiv.org/abs/2412.00770
Cardinality Estimation is to estimate the size of the output of a query without computing it, by using only statistics on the input relations. Existing estimators try to return an unbiased estimate of the cardinality: this is notoriously difficult. A
Externí odkaz:
http://arxiv.org/abs/2412.00642