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pro vyhledávání: '"Ratzlaff AN"'
Autor:
Ratzlaff, Neale, Olson, Matthew Lyle, Hinck, Musashi, Aflalo, Estelle, Tseng, Shao-Yen, Lal, Vasudev, Howard, Phillip
Large Multi-Modal Models (LMMs) have demonstrated impressive capabilities as general-purpose chatbots that can engage in conversations about a provided input, such as an image. However, their responses are influenced by societal biases present in the
Externí odkaz:
http://arxiv.org/abs/2411.12590
Autor:
Wargelin, B. J., Saar, S. H., Irving, Z. A., Slavin, J. D., Ratzlaff, P., Nascimento Jr, J. -D. do
Proxima Cen (GJ 551; dM5.5e) is one of only about a dozen fully convective stars known to have a stellar cycle, and the only one to have long-term X-ray monitoring. A previous analysis found that X-ray and mid-UV observations, particularly two epochs
Externí odkaz:
http://arxiv.org/abs/2411.04252
Autor:
Ratzlaff, Neale, Olson, Matthew Lyle, Hinck, Musashi, Tseng, Shao-Yen, Lal, Vasudev, Howard, Phillip
Large Vision Language Models (LVLMs) such as LLaVA have demonstrated impressive capabilities as general-purpose chatbots that can engage in conversations about a provided input image. However, their responses are influenced by societal biases present
Externí odkaz:
http://arxiv.org/abs/2410.13976
Autor:
Baker, Megan M., New, Alexander, Aguilar-Simon, Mario, Al-Halah, Ziad, Arnold, Sébastien M. R., Ben-Iwhiwhu, Ese, Brna, Andrew P., Brooks, Ethan, Brown, Ryan C., Daniels, Zachary, Daram, Anurag, Delattre, Fabien, Dellana, Ryan, Eaton, Eric, Fu, Haotian, Grauman, Kristen, Hostetler, Jesse, Iqbal, Shariq, Kent, Cassandra, Ketz, Nicholas, Kolouri, Soheil, Konidaris, George, Kudithipudi, Dhireesha, Learned-Miller, Erik, Lee, Seungwon, Littman, Michael L., Madireddy, Sandeep, Mendez, Jorge A., Nguyen, Eric Q., Piatko, Christine D., Pilly, Praveen K., Raghavan, Aswin, Rahman, Abrar, Ramakrishnan, Santhosh Kumar, Ratzlaff, Neale, Soltoggio, Andrea, Stone, Peter, Sur, Indranil, Tang, Zhipeng, Tiwari, Saket, Vedder, Kyle, Wang, Felix, Xu, Zifan, Yanguas-Gil, Angel, Yedidsion, Harel, Yu, Shangqun, Vallabha, Gautam K.
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original t
Externí odkaz:
http://arxiv.org/abs/2301.07799
Autor:
Vanessa Osmari, Fagner D’ambroso Fernandes, Maurício Tatto, Getúlio Dornelles Souza, Fabiana Raquel Ratzlaff, Jaíne Soares de Paula Vasconcellos, Sônia de Avila Botton, Diego Willian Nascimento Machado, Fernanda Silveira Flores Vogel, Luís Antônio Sangioni
Publikováno v:
Revista Brasileira de Parasitologia Veterinária, Vol 33, Iss 3 (2024)
Abstract Sand flies, vectors capable of transmitting Leishmania spp. and causing leishmaniasis, have been a concern in the central region of Rio Grande do Sul, where canine leishmaniasis (CanL) has been documented since 1985. Notably, there has been
Externí odkaz:
https://doaj.org/article/9512a2b0ade247778d50f0c3b50938a3
Autor:
Marshall, Herman L., Chen, Yang, Drake, Jeremy J., Guainazzi, Matteo, Kashyap, Vinay L., Meng, Xiao-Li, Plucinsky, Paul P., Ratzlaff, Peter, van Dyk, David A., Wang, Xufei
We describe a process for cross-calibrating the effective areas of X-ray telescopes that observe common targets. The targets are not assumed to be "standard candles" in the classic sense, in that we assume that the source fluxes have well-defined, bu
Externí odkaz:
http://arxiv.org/abs/2108.13476
Autor:
Olson, Matthew L., Nguyen, Thuy-Vy, Dixit, Gaurav, Ratzlaff, Neale, Wong, Weng-Keen, Kahng, Minsuk
Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data. However, detecting covariate shift is challenging, especially when the dat
Externí odkaz:
http://arxiv.org/abs/2108.08000
Recently, particle-based variational inference (ParVI) methods have gained interest because they can avoid arbitrary parametric assumptions that are common in variational inference. However, many ParVI approaches do not allow arbitrary sampling from
Externí odkaz:
http://arxiv.org/abs/2103.01291
Reward function specification can be difficult. Rewarding the agent for making a widget may be easy, but penalizing the multitude of possible negative side effects is hard. In toy environments, Attainable Utility Preservation (AUP) avoided side effec
Externí odkaz:
http://arxiv.org/abs/2006.06547
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