Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Cui, Claire"'
Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning. In this work, we propose a deep latent state-space generative model t
Externí odkaz:
http://arxiv.org/abs/2407.19371
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
Autor:
Zhou, Yanqi, Du, Nan, Huang, Yanping, Peng, Daiyi, Lan, Chang, Huang, Da, Shakeri, Siamak, So, David, Dai, Andrew, Lu, Yifeng, Chen, Zhifeng, Le, Quoc, Cui, Claire, Laudon, James, Dean, Jeff
Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-forward and self-attention in order to build a deep network. Here we inve
Externí odkaz:
http://arxiv.org/abs/2306.00008
Autor:
Kuo, Weicheng, Piergiovanni, AJ, Kim, Dahun, Luo, Xiyang, Caine, Ben, Li, Wei, Ogale, Abhijit, Zhou, Luowei, Dai, Andrew, Chen, Zhifeng, Cui, Claire, Angelova, Anelia
The development of language models have moved from encoder-decoder to decoder-only designs. In addition, we observe that the two most popular multimodal tasks, the generative and contrastive tasks, are nontrivial to accommodate in one architecture, a
Externí odkaz:
http://arxiv.org/abs/2303.16839
Autor:
Liu, Ruibo, Wei, Jason, Gu, Shixiang Shane, Wu, Te-Yen, Vosoughi, Soroush, Cui, Claire, Zhou, Denny, Dai, Andrew M.
Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current language models (LMs) miss the grounded experience of humans in the real-world -- their failure to re
Externí odkaz:
http://arxiv.org/abs/2210.05359
Autor:
Zhou, Denny, Schärli, Nathanael, Hou, Le, Wei, Jason, Scales, Nathan, Wang, Xuezhi, Schuurmans, Dale, Cui, Claire, Bousquet, Olivier, Le, Quoc, Chi, Ed
Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving problems harder than the exemplars shown in the prompts. To overcome t
Externí odkaz:
http://arxiv.org/abs/2205.10625
Autor:
Thoppilan, Romal, De Freitas, Daniel, Hall, Jamie, Shazeer, Noam, Kulshreshtha, Apoorv, Cheng, Heng-Tze, Jin, Alicia, Bos, Taylor, Baker, Leslie, Du, Yu, Li, YaGuang, Lee, Hongrae, Zheng, Huaixiu Steven, Ghafouri, Amin, Menegali, Marcelo, Huang, Yanping, Krikun, Maxim, Lepikhin, Dmitry, Qin, James, Chen, Dehao, Xu, Yuanzhong, Chen, Zhifeng, Roberts, Adam, Bosma, Maarten, Zhao, Vincent, Zhou, Yanqi, Chang, Chung-Ching, Krivokon, Igor, Rusch, Will, Pickett, Marc, Srinivasan, Pranesh, Man, Laichee, Meier-Hellstern, Kathleen, Morris, Meredith Ringel, Doshi, Tulsee, Santos, Renelito Delos, Duke, Toju, Soraker, Johnny, Zevenbergen, Ben, Prabhakaran, Vinodkumar, Diaz, Mark, Hutchinson, Ben, Olson, Kristen, Molina, Alejandra, Hoffman-John, Erin, Lee, Josh, Aroyo, Lora, Rajakumar, Ravi, Butryna, Alena, Lamm, Matthew, Kuzmina, Viktoriya, Fenton, Joe, Cohen, Aaron, Bernstein, Rachel, Kurzweil, Ray, Aguera-Arcas, Blaise, Cui, Claire, Croak, Marian, Chi, Ed, Le, Quoc
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. W
Externí odkaz:
http://arxiv.org/abs/2201.08239
Autor:
Du, Nan, Huang, Yanping, Dai, Andrew M., Tong, Simon, Lepikhin, Dmitry, Xu, Yuanzhong, Krikun, Maxim, Zhou, Yanqi, Yu, Adams Wei, Firat, Orhan, Zoph, Barret, Fedus, Liam, Bosma, Maarten, Zhou, Zongwei, Wang, Tao, Wang, Yu Emma, Webster, Kellie, Pellat, Marie, Robinson, Kevin, Meier-Hellstern, Kathleen, Duke, Toju, Dixon, Lucas, Zhang, Kun, Le, Quoc V, Wu, Yonghui, Chen, Zhifeng, Cui, Claire
Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. However, training th
Externí odkaz:
http://arxiv.org/abs/2112.06905
We present a task and benchmark dataset for person-centric visual grounding, the problem of linking between people named in a caption and people pictured in an image. In contrast to prior work in visual grounding, which is predominantly object-based,
Externí odkaz:
http://arxiv.org/abs/2108.07253
Autor:
Chen, Zhe, Wang, Yuyan, Lin, Dong, Cheng, Derek Zhiyuan, Hong, Lichan, Chi, Ed H., Cui, Claire
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results. Ensembl
Externí odkaz:
http://arxiv.org/abs/2008.07032