Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Dean, Jeff"'
Autor:
Pailes, Matthew C., Norman, Laura M., Baisan, Christopher H., Meko, David M., Gauthier, Nicolas, Villanueva- Diaz, Jose, Dean, Jeff, Martínez, Jupiter, Kessler, Nicholas V., Towner, Ron
In arid and semiarid landscapes, water is the primary limiting resource for human activity and ecosystem functioning. More than 40% of the world’s population lives in dryland environments (White and Nackoney, 2003). In these landscapes annual rainf
Externí odkaz:
https://library.oapen.org/handle/20.500.12657/77024
Autor:
Gemma Team, Riviere, Morgane, Pathak, Shreya, Sessa, Pier Giuseppe, Hardin, Cassidy, Bhupatiraju, Surya, Hussenot, Léonard, Mesnard, Thomas, Shahriari, Bobak, Ramé, Alexandre, Ferret, Johan, Liu, Peter, Tafti, Pouya, Friesen, Abe, Casbon, Michelle, Ramos, Sabela, Kumar, Ravin, Lan, Charline Le, Jerome, Sammy, Tsitsulin, Anton, Vieillard, Nino, Stanczyk, Piotr, Girgin, Sertan, Momchev, Nikola, Hoffman, Matt, Thakoor, Shantanu, Grill, Jean-Bastien, Neyshabur, Behnam, Bachem, Olivier, Walton, Alanna, Severyn, Aliaksei, Parrish, Alicia, Ahmad, Aliya, Hutchison, Allen, Abdagic, Alvin, Carl, Amanda, Shen, Amy, Brock, Andy, Coenen, Andy, Laforge, Anthony, Paterson, Antonia, Bastian, Ben, Piot, Bilal, Wu, Bo, Royal, Brandon, Chen, Charlie, Kumar, Chintu, Perry, Chris, Welty, Chris, Choquette-Choo, Christopher A., Sinopalnikov, Danila, Weinberger, David, Vijaykumar, Dimple, Rogozińska, Dominika, Herbison, Dustin, Bandy, Elisa, Wang, Emma, Noland, Eric, Moreira, Erica, Senter, Evan, Eltyshev, Evgenii, Visin, Francesco, Rasskin, Gabriel, Wei, Gary, Cameron, Glenn, Martins, Gus, Hashemi, Hadi, Klimczak-Plucińska, Hanna, Batra, Harleen, Dhand, Harsh, Nardini, Ivan, Mein, Jacinda, Zhou, Jack, Svensson, James, Stanway, Jeff, Chan, Jetha, Zhou, Jin Peng, Carrasqueira, Joana, Iljazi, Joana, Becker, Jocelyn, Fernandez, Joe, van Amersfoort, Joost, Gordon, Josh, Lipschultz, Josh, Newlan, Josh, Ji, Ju-yeong, Mohamed, Kareem, Badola, Kartikeya, Black, Kat, Millican, Katie, McDonell, Keelin, Nguyen, Kelvin, Sodhia, Kiranbir, Greene, Kish, Sjoesund, Lars Lowe, Usui, Lauren, Sifre, Laurent, Heuermann, Lena, Lago, Leticia, McNealus, Lilly, Soares, Livio Baldini, Kilpatrick, Logan, Dixon, Lucas, Martins, Luciano, Reid, Machel, Singh, Manvinder, Iverson, Mark, Görner, Martin, Velloso, Mat, Wirth, Mateo, Davidow, Matt, Miller, Matt, Rahtz, Matthew, Watson, Matthew, Risdal, Meg, Kazemi, Mehran, Moynihan, Michael, Zhang, Ming, Kahng, Minsuk, Park, Minwoo, Rahman, Mofi, Khatwani, Mohit, Dao, Natalie, Bardoliwalla, Nenshad, Devanathan, Nesh, Dumai, Neta, Chauhan, Nilay, Wahltinez, Oscar, Botarda, Pankil, Barnes, Parker, Barham, Paul, Michel, Paul, Jin, Pengchong, Georgiev, Petko, Culliton, Phil, Kuppala, Pradeep, Comanescu, Ramona, Merhej, Ramona, Jana, Reena, Rokni, Reza Ardeshir, Agarwal, Rishabh, Mullins, Ryan, Saadat, Samaneh, Carthy, Sara Mc, Perrin, Sarah, Arnold, Sébastien M. R., Krause, Sebastian, Dai, Shengyang, Garg, Shruti, Sheth, Shruti, Ronstrom, Sue, Chan, Susan, Jordan, Timothy, Yu, Ting, Eccles, Tom, Hennigan, Tom, Kocisky, Tomas, Doshi, Tulsee, Jain, Vihan, Yadav, Vikas, Meshram, Vilobh, Dharmadhikari, Vishal, Barkley, Warren, Wei, Wei, Ye, Wenming, Han, Woohyun, Kwon, Woosuk, Xu, Xiang, Shen, Zhe, Gong, Zhitao, Wei, Zichuan, Cotruta, Victor, Kirk, Phoebe, Rao, Anand, Giang, Minh, Peran, Ludovic, Warkentin, Tris, Collins, Eli, Barral, Joelle, Ghahramani, Zoubin, Hadsell, Raia, Sculley, D., Banks, Jeanine, Dragan, Anca, Petrov, Slav, Vinyals, Oriol, Dean, Jeff, Hassabis, Demis, Kavukcuoglu, Koray, Farabet, Clement, Buchatskaya, Elena, Borgeaud, Sebastian, Fiedel, Noah, Joulin, Armand, Kenealy, Kathleen, Dadashi, Robert, Andreev, Alek
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the
Externí odkaz:
http://arxiv.org/abs/2408.00118
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:
Gemma Team, Mesnard, Thomas, Hardin, Cassidy, Dadashi, Robert, Bhupatiraju, Surya, Pathak, Shreya, Sifre, Laurent, Rivière, Morgane, Kale, Mihir Sanjay, Love, Juliette, Tafti, Pouya, Hussenot, Léonard, Sessa, Pier Giuseppe, Chowdhery, Aakanksha, Roberts, Adam, Barua, Aditya, Botev, Alex, Castro-Ros, Alex, Slone, Ambrose, Héliou, Amélie, Tacchetti, Andrea, Bulanova, Anna, Paterson, Antonia, Tsai, Beth, Shahriari, Bobak, Lan, Charline Le, Choquette-Choo, Christopher A., Crepy, Clément, Cer, Daniel, Ippolito, Daphne, Reid, David, Buchatskaya, Elena, Ni, Eric, Noland, Eric, Yan, Geng, Tucker, George, Muraru, George-Christian, Rozhdestvenskiy, Grigory, Michalewski, Henryk, Tenney, Ian, Grishchenko, Ivan, Austin, Jacob, Keeling, James, Labanowski, Jane, Lespiau, Jean-Baptiste, Stanway, Jeff, Brennan, Jenny, Chen, Jeremy, Ferret, Johan, Chiu, Justin, Mao-Jones, Justin, Lee, Katherine, Yu, Kathy, Millican, Katie, Sjoesund, Lars Lowe, Lee, Lisa, Dixon, Lucas, Reid, Machel, Mikuła, Maciej, Wirth, Mateo, Sharman, Michael, Chinaev, Nikolai, Thain, Nithum, Bachem, Olivier, Chang, Oscar, Wahltinez, Oscar, Bailey, Paige, Michel, Paul, Yotov, Petko, Chaabouni, Rahma, Comanescu, Ramona, Jana, Reena, Anil, Rohan, McIlroy, Ross, Liu, Ruibo, Mullins, Ryan, Smith, Samuel L, Borgeaud, Sebastian, Girgin, Sertan, Douglas, Sholto, Pandya, Shree, Shakeri, Siamak, De, Soham, Klimenko, Ted, Hennigan, Tom, Feinberg, Vlad, Stokowiec, Wojciech, Chen, Yu-hui, Ahmed, Zafarali, Gong, Zhitao, Warkentin, Tris, Peran, Ludovic, Giang, Minh, Farabet, Clément, Vinyals, Oriol, Dean, Jeff, Kavukcuoglu, Koray, Hassabis, Demis, Ghahramani, Zoubin, Eck, Douglas, Barral, Joelle, Pereira, Fernando, Collins, Eli, Joulin, Armand, Fiedel, Noah, Senter, Evan, Andreev, Alek, Kenealy, Kathleen
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding,
Externí odkaz:
http://arxiv.org/abs/2403.08295
AI and the Opportunity for Shared Prosperity: Lessons from the History of Technology and the Economy
Recent progress in artificial intelligence (AI) marks a pivotal moment in human history. It presents the opportunity for machines to learn, adapt, and perform tasks that have the potential to assist people, from everyday activities to their most crea
Externí odkaz:
http://arxiv.org/abs/2401.09718
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:
Pope, Reiner, Douglas, Sholto, Chowdhery, Aakanksha, Devlin, Jacob, Bradbury, James, Levskaya, Anselm, Heek, Jonathan, Xiao, Kefan, Agrawal, Shivani, Dean, Jeff
We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long sequence lengths. Better understanding of the engineering tradeoffs for inf
Externí odkaz:
http://arxiv.org/abs/2211.05102
Autor:
Chung, Hyung Won, Hou, Le, Longpre, Shayne, Zoph, Barret, Tay, Yi, Fedus, William, Li, Yunxuan, Wang, Xuezhi, Dehghani, Mostafa, Brahma, Siddhartha, Webson, Albert, Gu, Shixiang Shane, Dai, Zhuyun, Suzgun, Mirac, Chen, Xinyun, Chowdhery, Aakanksha, Castro-Ros, Alex, Pellat, Marie, Robinson, Kevin, Valter, Dasha, Narang, Sharan, Mishra, Gaurav, Yu, Adams, Zhao, Vincent, Huang, Yanping, Dai, Andrew, Yu, Hongkun, Petrov, Slav, Chi, Ed H., Dean, Jeff, Devlin, Jacob, Roberts, Adam, Zhou, Denny, Le, Quoc V., Wei, Jason
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the
Externí odkaz:
http://arxiv.org/abs/2210.11416
Sparse expert models are a thirty-year old concept re-emerging as a popular architecture in deep learning. This class of architecture encompasses Mixture-of-Experts, Switch Transformers, Routing Networks, BASE layers, and others, all with the unifyin
Externí odkaz:
http://arxiv.org/abs/2209.01667
Autor:
Wei, Jason, Tay, Yi, Bommasani, Rishi, Raffel, Colin, Zoph, Barret, Borgeaud, Sebastian, Yogatama, Dani, Bosma, Maarten, Zhou, Denny, Metzler, Donald, Chi, Ed H., Hashimoto, Tatsunori, Vinyals, Oriol, Liang, Percy, Dean, Jeff, Fedus, William
Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead discusses an unpredictable phenomenon that we refer to as emergent abilities of large language
Externí odkaz:
http://arxiv.org/abs/2206.07682