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pro vyhledávání: '"Wilson, Daniel"'
Establishing the frequentist properties of Bayesian approaches widens their appeal and offers new understanding. In hypothesis testing, Bayesian model averaging addresses the problem that conclusions are sensitive to variable selection. But Bayesian
Externí odkaz:
http://arxiv.org/abs/2312.17566
Autor:
Wilson, Daniel C.
Top high performance computing (HPC) data centers recently entered the era of exascale computing, requiring up to tens of megawatts for a computing facility to meet its users’ computing needs. The massive capacity for power at a single site comes w
Externí odkaz:
https://hdl.handle.net/2144/46654
Autor:
Daly, R. Terik, Ernst, Carolyn M., Barnouin, Olivier S., Chabot, Nancy L., Rivkin, Andrew S., Cheng, Andrew F., Adams, Elena Y., Agrusa, Harrison F., Abel, Elisabeth D., Alford, Amy L., Asphaug, Erik I., Atchison, Justin A., Badger, Andrew R., Baki, Paul, Ballouz, Ronald-L., Bekker, Dmitriy L., Bellerose, Julie, Bhaskaran, Shyam, Buratti, Bonnie J., Cambioni, Saverio, Chen, Michelle H., Chesley, Steven R., Chiu, George, Collins, Gareth S., Cox, Matthew W., DeCoster, Mallory E., Ericksen, Peter S., Espiritu, Raymond C., Faber, Alan S., Farnham, Tony L., Ferrari, Fabio, Fletcher, Zachary J., Gaskell, Robert W., Graninger, Dawn M., Haque, Musad A., Harrington-Duff, Patricia A., Hefter, Sarah, Herreros, Isabel, Hirabayashi, Masatoshi, Huang, Philip M., Hsieh, Syau-Yun W., Jacobson, Seth A., Jenkins, Stephen N., Jensenius, Mark A., John, Jeremy W., Jutzi, Martin, Kohout, Tomas, Krueger, Timothy O., Laipert, Frank E., Lopez, Norberto R., Luther, Robert, Lucchetti, Alice, Mages, Declan M., Marchi, Simone, Martin, Anna C., McQuaide, Maria E., Michel, Patrick, Moskovitz, Nicholas A., Murphy, Ian W., Murdoch, Naomi, Naidu, Shantanu P., Nair, Hari, Nolan, Michael C., Ormö, Jens, Pajola, Maurizio, Palmer, Eric E., Peachey, James M., Pravec, Petr, Raducan, Sabina D., Ramesh, K. T., Ramirez, Joshua R., Reynolds, Edward L., Richman, Joshua E., Robin, Colas Q., Rodriguez, Luis M., Roufberg, Lew M., Rush, Brian P., Sawyer, Carolyn A., Scheeres, Daniel J., Scheirich, Petr, Schwartz, Stephen R., Shannon, Matthew P., Shapiro, Brett N., Shearer, Caitlin E., Smith, Evan J., Steele, R. Joshua, Steckloff, Jordan K, Stickle, Angela M., Sunshine, Jessica M., Superfin, Emil A., Tarzi, Zahi B., Thomas, Cristina A., Thomas, Justin R., Trigo-Rodríguez, Josep M., Tropf, B. Teresa, Vaughan, Andrew T., Velez, Dianna, Waller, C. Dany, Wilson, Daniel S., Wortman, Kristin A., Zhang, Yun
While no known asteroid poses a threat to Earth for at least the next century, the catalog of near-Earth asteroids is incomplete for objects whose impacts would produce regional devastation. Several approaches have been proposed to potentially preven
Externí odkaz:
http://arxiv.org/abs/2303.02248
State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning (DL) or Riemannian-Geometry-based decoders (RBDs). Recently, there is growing interest in Deep Riemannian Networks (DRNs
Externí odkaz:
http://arxiv.org/abs/2212.10426