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Genuine multipartite entanglement is a valuable resource in quantum information science, as it exhibits stronger non-locality compared to bipartite entanglement. This non-locality can be exploited in various quantum information protocols, such as tel
Externí odkaz:
http://arxiv.org/abs/2409.02417
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, 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, Lu, Peggy, Lum, Kristian, Ma, Yukun, Malik, Chase, Mellor, John, Mensink, Thomas, Mosseri, Inbar, Murray, Tom, Nematzadeh, Aida, Nicholas, Paul, 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, 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:
Vasconcelos, Cristina N., Rashwan, Abdullah, Waters, Austin, Walker, Trevor, Xu, Keyang, Yan, Jimmy, Qian, Rui, Luo, Shixin, Parekh, Zarana, Bunner, Andrew, Fei, Hongliang, Garg, Roopal, Guo, Mandy, Kajic, Ivana, Li, Yeqing, Nandwani, Henna, Pont-Tuset, Jordi, Onoe, Yasumasa, Rosston, Sarah, Wang, Su, Zhou, Wenlei, Swersky, Kevin, Fleet, David J., Baldridge, Jason M., Wang, Oliver
We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable training of large-scale, high-resolution models. without the needs for cascad
Externí odkaz:
http://arxiv.org/abs/2405.16759
Autor:
Garg, Roopal, Burns, Andrea, Ayan, Burcu Karagol, Bitton, Yonatan, Montgomery, Ceslee, Onoe, Yasumasa, Bunner, Andrew, Krishna, Ranjay, Baldridge, Jason, Soricut, Radu
Despite the longstanding adage "an image is worth a thousand words," generating accurate hyper-detailed image descriptions remains unsolved. Trained on short web-scraped image text, vision-language models often generate incomplete descriptions with v
Externí odkaz:
http://arxiv.org/abs/2405.02793
Autor:
Onoe, Yasumasa, Rane, Sunayana, Berger, Zachary, Bitton, Yonatan, Cho, Jaemin, Garg, Roopal, Ku, Alexander, Parekh, Zarana, Pont-Tuset, Jordi, Tanzer, Garrett, Wang, Su, Baldridge, Jason
Vision-language datasets are vital for both text-to-image (T2I) and image-to-text (I2T) research. However, current datasets lack descriptions with fine-grained detail that would allow for richer associations to be learned by models. To fill the gap,
Externí odkaz:
http://arxiv.org/abs/2404.19753
Autor:
Wiles, Olivia, Zhang, Chuhan, Albuquerque, Isabela, Kajić, Ivana, Wang, Su, Bugliarello, Emanuele, Onoe, Yasumasa, Knutsen, Chris, Rashtchian, Cyrus, Pont-Tuset, Jordi, Nematzadeh, Aida
While text-to-image (T2I) generative models have become ubiquitous, they do not necessarily generate images that align with a given prompt. While previous work has evaluated T2I alignment by proposing metrics, benchmarks, and templates for collecting
Externí odkaz:
http://arxiv.org/abs/2404.16820
Autor:
JiHye Kim
Publikováno v:
The Korean Journal of Japanology. 134:105-124
Publikováno v:
NTT Technical Review. Jan2024, Vol. 22 Issue 1, p12-17. 6p.
Autor:
Lai, S., Utehs, J., Wilhahn, A., Bach, O., Brianne, E., Ebrahimi, A., Gadow, K., Göttlicher, P., Hartbrich, O., Heuchel, D., Irles, A., Krüger, K., Kvasnicka, J., Lu, S., Neubüser, C., Provenza, A., Reinecke, M., Sefkow, F., Schuwalow, S., De Silva, M., Sudo, Y., Tran, H. L., Buhmann, E., Garutti, E., Huck, S., Kasieczka, G., Martens, S., Rolph, J., Wellhausen, J., Blazey, G. C., Dyshkant, A., Francis, K., Zutshi, V., Bilki, B., Northacker, D., Onel, Y., Hummer, F., Simon, F., Kawagoe, K., Onoe, T., Suehara, T., Tsumura, S., Yoshioka, T., Fouz, M. C., Emberger, L., Graf, C., Wagner, M., Pöschl, R., Richard, F., Zerwas, D., Boudry, V., Brient, J-C., Nanni, J., Videau, H., Liu, L., Masuda, R., Murata, T., Ootani, W., Takatsu, T., Tsuji, N., Chadeeva, M., Danilov, M., Korpachev, S., Rusinov, V.
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to
Externí odkaz:
http://arxiv.org/abs/2403.04632