Zobrazeno 1 - 10
of 13 404
pro vyhledávání: '"Cheng Yong In"'
In this study, we introduce a novel methodological framework called Bayesian Penalized Empirical Likelihood (BPEL), designed to address the computational challenges inherent in empirical likelihood (EL) approaches. Our approach has two primary object
Externí odkaz:
http://arxiv.org/abs/2412.17354
We investigate spontaneous scalarization in the Einstein-Born-Infeld-Scalar (EBIS) model with asymptotically AdS boundary conditions, revealing novel dynamical critical phenomena in black hole evolution. Through numerical analysis, we discover a dist
Externí odkaz:
http://arxiv.org/abs/2412.02132
Autor:
Palepu, Anil, Dhillon, Vikram, Niravath, Polly, Weng, Wei-Hung, Prasad, Preethi, Saab, Khaled, Tanno, Ryutaro, Cheng, Yong, Mai, Hanh, Burns, Ethan, Ajmal, Zainub, Kulkarni, Kavita, Mansfield, Philip, Webster, Dale, Barral, Joelle, Gottweis, Juraj, Schaekermann, Mike, Mahdavi, S. Sara, Natarajan, Vivek, Karthikesalingam, Alan, Tu, Tao
Large language models (LLMs) have shown remarkable progress in encoding clinical knowledge and responding to complex medical queries with appropriate clinical reasoning. However, their applicability in subspecialist or complex medical settings remain
Externí odkaz:
http://arxiv.org/abs/2411.03395
Based on the hyperboloidal framework, we research the dynamical process of charged de Sitter black holes scattered by a charged scalar field. From the linear perturbation analysis, with the coupling strength within a critical interval, the charged sc
Externí odkaz:
http://arxiv.org/abs/2411.03193
We investigate spontaneous vectorization in the Einstein-Maxwell-Vector (EMV) model, introducing a novel mechanism driven by the interplay between electromagnetic and vector fields. A key innovation in our work is the resolution of an apparent diverg
Externí odkaz:
http://arxiv.org/abs/2410.16920
Two-party split learning has emerged as a popular paradigm for vertical federated learning. To preserve the privacy of the label owner, split learning utilizes a split model, which only requires the exchange of intermediate representations (IRs) base
Externí odkaz:
http://arxiv.org/abs/2410.09125
Autor:
O'Sullivan, Jack W., Palepu, Anil, Saab, Khaled, Weng, Wei-Hung, Cheng, Yong, Chu, Emily, Desai, Yaanik, Elezaby, Aly, Kim, Daniel Seung, Lan, Roy, Tang, Wilson, Tapaskar, Natalie, Parikh, Victoria, Jain, Sneha S., Kulkarni, Kavita, Mansfield, Philip, Webster, Dale, Gottweis, Juraj, Barral, Joelle, Schaekermann, Mike, Tanno, Ryutaro, Mahdavi, S. Sara, Natarajan, Vivek, Karthikesalingam, Alan, Ashley, Euan, Tu, Tao
The scarcity of subspecialist medical expertise, particularly in rare, complex and life-threatening diseases, poses a significant challenge for healthcare delivery. This issue is particularly acute in cardiology where timely, accurate management dete
Externí odkaz:
http://arxiv.org/abs/2410.03741
Autor:
Tang, Yunhao, Guo, Daniel Zhaohan, Zheng, Zeyu, Calandriello, Daniele, Cao, Yuan, Tarassov, Eugene, Munos, Rémi, Pires, Bernardo Ávila, Valko, Michal, Cheng, Yong, Dabney, Will
Reinforcement learning from human feedback (RLHF) is the canonical framework for large language model alignment. However, rising popularity in offline alignment algorithms challenge the need for on-policy sampling in RLHF. Within the context of rewar
Externí odkaz:
http://arxiv.org/abs/2405.08448
Akademický článek
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Autor:
Saab, Khaled, Tu, Tao, Weng, Wei-Hung, Tanno, Ryutaro, Stutz, David, Wulczyn, Ellery, Zhang, Fan, Strother, Tim, Park, Chunjong, Vedadi, Elahe, Chaves, Juanma Zambrano, Hu, Szu-Yeu, Schaekermann, Mike, Kamath, Aishwarya, Cheng, Yong, Barrett, David G. T., Cheung, Cathy, Mustafa, Basil, Palepu, Anil, McDuff, Daniel, Hou, Le, Golany, Tomer, Liu, Luyang, Alayrac, Jean-baptiste, Houlsby, Neil, Tomasev, Nenad, Freyberg, Jan, Lau, Charles, Kemp, Jonas, Lai, Jeremy, Azizi, Shekoofeh, Kanada, Kimberly, Man, SiWai, Kulkarni, Kavita, Sun, Ruoxi, Shakeri, Siamak, He, Luheng, Caine, Ben, Webson, Albert, Latysheva, Natasha, Johnson, Melvin, Mansfield, Philip, Lu, Jian, Rivlin, Ehud, Anderson, Jesper, Green, Bradley, Wong, Renee, Krause, Jonathan, Shlens, Jonathon, Dominowska, Ewa, Eslami, S. M. Ali, Chou, Katherine, Cui, Claire, Vinyals, Oriol, Kavukcuoglu, Koray, Manyika, James, Dean, Jeff, Hassabis, Demis, Matias, Yossi, Webster, Dale, Barral, Joelle, Corrado, Greg, Semturs, Christopher, Mahdavi, S. Sara, Gottweis, Juraj, Karthikesalingam, Alan, Natarajan, Vivek
Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilit
Externí odkaz:
http://arxiv.org/abs/2404.18416