Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Ryu, MoonKyung"'
Autor:
Nabati, Ofir, Tennenholtz, Guy, Hsu, ChihWei, Ryu, Moonkyung, Ramachandran, Deepak, Chow, Yinlam, Li, Xiang, Boutilier, Craig
We address the problem of personalized, interactive text-to-image (T2I) generation, designing a reinforcement learning (RL) agent which iteratively improves a set of generated images for a user through a sequence of prompt expansions. Using human rat
Externí odkaz:
http://arxiv.org/abs/2412.10419
Autor:
Imagen-Team-Google, Baldridge, Jason, Bauer, Jakob, Bhutani, Mukul, Brichtova, Nicole, Bunner, Andrew, Castrejon, Lluis, Chan, Kelvin, Chen, Yichang, Dieleman, Sander, Du, Yuqing, Eaton-Rosen, Zach, Fei, Hongliang, de Freitas, Nando, Gao, Yilin, Gladchenko, Evgeny, Colmenarejo, Sergio Gómez, Guo, Mandy, Haig, Alex, Hawkins, Will, Hu, Hexiang, Huang, Huilian, Igwe, Tobenna Peter, Kaplanis, Christos, Khodadadeh, Siavash, Kim, Yelin, Konyushkova, Ksenia, Langner, Karol, Lau, Eric, Lawton, Rory, Luo, Shixin, Mokrá, Soňa, Nandwani, Henna, Onoe, Yasumasa, Oord, Aäron van den, Parekh, Zarana, Pont-Tuset, Jordi, Qi, Hang, Qian, Rui, Ramachandran, Deepak, Rane, Poorva, Rashwan, Abdullah, Razavi, Ali, Riachi, Robert, Srinivasan, Hansa, Srinivasan, Srivatsan, Strudel, Robin, Uria, Benigno, Wang, Oliver, Wang, Su, Waters, Austin, Wolff, Chris, Wright, Auriel, Xiao, Zhisheng, Xiong, Hao, Xu, Keyang, van Zee, Marc, Zhang, Junlin, Zhang, Katie, Zhou, Wenlei, Zolna, Konrad, Aboubakar, Ola, Akbulut, Canfer, Akerlund, Oscar, Albuquerque, Isabela, Anderson, Nina, Andreetto, Marco, Aroyo, Lora, Bariach, Ben, Barker, David, Ben, Sherry, Berman, Dana, Biles, Courtney, Blok, Irina, Botadra, Pankil, Brennan, Jenny, Brown, Karla, Buckley, John, Bunel, Rudy, Bursztein, Elie, Butterfield, Christina, Caine, Ben, Carpenter, Viral, Casagrande, Norman, Chang, Ming-Wei, Chang, Solomon, Chaudhuri, Shamik, Chen, Tony, Choi, John, Churbanau, Dmitry, Clement, Nathan, Cohen, Matan, Cole, Forrester, Dektiarev, Mikhail, Du, Vincent, Dutta, Praneet, Eccles, Tom, Elue, Ndidi, Feden, Ashley, Fruchter, Shlomi, Garcia, Frankie, Garg, Roopal, Ge, Weina, Ghazy, Ahmed, Gipson, Bryant, Goodman, Andrew, Górny, Dawid, Gowal, Sven, Gupta, Khyatti, Halpern, Yoni, Han, Yena, Hao, Susan, Hayes, Jamie, Heek, Jonathan, Hertz, Amir, Hirst, Ed, Hoogeboom, Emiel, Hou, Tingbo, Howard, Heidi, Ibrahim, Mohamed, Ike-Njoku, Dirichi, Iljazi, Joana, Ionescu, Vlad, Isaac, William, Jana, Reena, Jennings, Gemma, Jenson, Donovon, Jia, Xuhui, Jones, Kerry, Ju, Xiaoen, Kajic, Ivana, Ayan, Burcu Karagol, Kelly, Jacob, Kothawade, Suraj, Kouridi, Christina, Ktena, Ira, Kumakaw, Jolanda, Kurniawan, Dana, Lagun, Dmitry, Lavitas, Lily, Lee, Jason, Li, Tao, Liang, Marco, Li-Calis, Maggie, Liu, Yuchi, Alberca, Javier Lopez, Lorrain, Matthieu Kim, Lu, Peggy, Lum, Kristian, Ma, Yukun, Malik, Chase, Mellor, John, Mensink, Thomas, Mosseri, Inbar, Murray, Tom, Nematzadeh, Aida, Nicholas, Paul, Nørly, Signe, Oliveira, João Gabriel, Ortiz-Jimenez, Guillermo, Paganini, Michela, Paine, Tom Le, Paiss, Roni, Parrish, Alicia, Peckham, Anne, Peswani, Vikas, Petrovski, Igor, Pfaff, Tobias, Pirozhenko, Alex, Poplin, Ryan, Prabhu, Utsav, Qi, Yuan, Rahtz, Matthew, Rashtchian, Cyrus, Rastogi, Charvi, Raul, Amit, Rebuffi, Sylvestre-Alvise, Ricco, Susanna, Riedel, Felix, Robinson, Dirk, Rohatgi, Pankaj, Rosgen, Bill, Rumbley, Sarah, Ryu, Moonkyung, Salgado, Anthony, Salimans, Tim, Singla, Sahil, Schroff, Florian, Schumann, Candice, Shah, Tanmay, Shaw, Eleni, Shaw, Gregory, Shillingford, Brendan, Shivakumar, Kaushik, Shtatnov, Dennis, Singer, Zach, Sluzhaev, Evgeny, Sokolov, Valerii, Sottiaux, Thibault, Stimberg, Florian, Stone, Brad, Stutz, David, Su, Yu-Chuan, Tabellion, Eric, Tang, Shuai, Tao, David, Thomas, Kurt, Thornton, Gregory, Toor, Andeep, Udrescu, Cristian, Upadhyay, Aayush, Vasconcelos, Cristina, Vasiloff, Alex, Voynov, Andrey, Walker, Amanda, Wang, Luyu, Wang, Miaosen, Wang, Simon, Wang, Stanley, Wang, Qifei, Wang, Yuxiao, Weisz, Ágoston, Wiles, Olivia, Wu, Chenxia, Xu, Xingyu Federico, Xue, Andrew, Yang, Jianbo, Yu, Luo, Yurtoglu, Mete, Zand, Ali, Zhang, Han, Zhang, Jiageng, Zhao, Catherine, Zhaxybay, Adilet, Zhou, Miao, Zhu, Shengqi, Zhu, Zhenkai, Bloxwich, Dawn, Bordbar, Mahyar, Cobo, Luis C., Collins, Eli, Dai, Shengyang, Doshi, Tulsee, Dragan, Anca, Eck, Douglas, Hassabis, Demis, Hsiao, Sissie, Hume, Tom, Kavukcuoglu, Koray, King, Helen, Krawczyk, Jack, Li, Yeqing, Meier-Hellstern, Kathy, Orban, Andras, Pinsky, Yury, Subramanya, Amar, Vinyals, Oriol, Yu, Ting, Zwols, Yori
We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. I
Externí odkaz:
http://arxiv.org/abs/2408.07009
Autor:
Fan, Ying, Watkins, Olivia, Du, Yuqing, Liu, Hao, Ryu, Moonkyung, Boutilier, Craig, Abbeel, Pieter, Ghavamzadeh, Mohammad, Lee, Kangwook, Lee, Kimin
Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and then improve the models based on the learned reward function. Even though
Externí odkaz:
http://arxiv.org/abs/2305.16381
Autor:
Lee, Kimin, Liu, Hao, Ryu, Moonkyung, Watkins, Olivia, Du, Yuqing, Boutilier, Craig, Abbeel, Pieter, Ghavamzadeh, Mohammad, Gu, Shixiang Shane
Deep generative models have shown impressive results in text-to-image synthesis. However, current text-to-image models often generate images that are inadequately aligned with text prompts. We propose a fine-tuning method for aligning such models usi
Externí odkaz:
http://arxiv.org/abs/2302.12192
Autor:
Cohen, Deborah, Ryu, Moonkyung, Chow, Yinlam, Keller, Orgad, Greenberg, Ido, Hassidim, Avinatan, Fink, Michael, Matias, Yossi, Szpektor, Idan, Boutilier, Craig, Elidan, Gal
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a
Externí odkaz:
http://arxiv.org/abs/2208.02294
Autor:
Chow, Yinlam, Tulepbergenov, Aza, Nachum, Ofir, Ryu, MoonKyung, Ghavamzadeh, Mohammad, Boutilier, Craig
Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use reinforcement learning (RL) to develop a dialogue agent that avoids bei
Externí odkaz:
http://arxiv.org/abs/2206.00059
Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL. However, designing such algorithms is ofte
Externí odkaz:
http://arxiv.org/abs/2006.05443
Value-based reinforcement learning (RL) methods like Q-learning have shown success in a variety of domains. One challenge in applying Q-learning to continuous-action RL problems, however, is the continuous action maximization (max-Q) required for opt
Externí odkaz:
http://arxiv.org/abs/1909.12397
Publikováno v:
Proceedings of the Second Annual ACM Conference: Multimedia Systems; 2/23/2011, p175-186, 12p
Publikováno v:
ACM SIGMETRICS Performance Evaluation Review; June 2012, Vol. 40 Issue: 1 p235-246, 12p