Zobrazeno 1 - 10
of 35 528
pro vyhledávání: '"Razavi A"'
Autor:
Wickremasinghe, Dewmini Hasara, Xu, Yiyang, Puyol-Antón, Esther, Aljabar, Paul, Razavi, Reza, King, Andrew P.
Quantification of cardiac biomarkers from cine cardiovascular magnetic resonance (CMR) data using deep learning (DL) methods offers many advantages, such as increased accuracy and faster analysis. However, only a few studies have focused on the scan-
Externí odkaz:
http://arxiv.org/abs/2408.11754
Autor:
Jafarinia, Hossein, Alipanah, Alireza, Hamdi, Danial, Razavi, Saeed, Mirzaie, Nahal, Rohban, Mohammad Hossein
Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensive self-supervised learning (SSL) for satisfactory performance, requir
Externí odkaz:
http://arxiv.org/abs/2408.08258
Autor:
Imagen-Team-Google, Baldridge, Jason, Bauer, Jakob, Bhutani, Mukul, Brichtova, Nicole, Bunner, Andrew, 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, Hertz, Amir, Hirst, Ed, 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, 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, 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:
Charles, N., Kern, N. S., Pascua, R., Bernardi, G., Bester, L., Smirnov, O., Acedo, E. d. L., Abdurashidova, Z., Adams, T., Aguirre, J. E., Baartman, R., Beardsley, A. P., Berkhout, L. M., Billings, T. S., Bowman, J. D., Bull, P., Burba, J., Byrne, R., Carey, S., Chen, K., Choudhuri, S., Cox, T., DeBoer, D. R., Dexter, M., Dillon, J. S., Dynes, S., Eksteen, N., Ely, J., Ewall-Wice, A., Fritz, R., Furlanetto, S. R., Gale-Sides, K., Garsden, H., Gehlot, B. K., Ghosh, A., Gorce, A., Gorthi, D., Halday, Z., Hazelton, B. J., Hewitt, J. N., Hickish, J., Huang, T., Jacobs, D. C., Josaitis, A., Kerrigan, J., Kittiwisit, P., Kolopanis, M., Lanman, A., Liu, A., Ma, Y. -Z., MacMahon, D. H. E., Malan, L., Malgas, K., Malgas, C., Marero, B., Martinot, Z. E., McBride, L., Mesinger, A., Mohamed-Hinds, N., Molewa, M., Morales, M. F., Murray, S., Nikolic, B., Nuwegeld, H., Parsons, A. R., Patra, N., Plante, P. L., Qin, Y., Rath, E., Razavi-Ghods, N., Riley, D., Robnett, J., Rosie, K., Santos, M. G., Sims, P., Singh, S., Storer, D., Swarts, H., Tan, J., Wilensky, M. J., Williams, P. K. G., Wyngaarden, P. v., Zheng, H.
The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Arra
Externí odkaz:
http://arxiv.org/abs/2407.20923
Inference serving is of great importance in deploying machine learning models in real-world applications, ensuring efficient processing and quick responses to inference requests. However, managing resources in these systems poses significant challeng
Externí odkaz:
http://arxiv.org/abs/2407.14843
Quantum Data Centers (QDCs) could overcome the scalability challenges of modern quantum computers. Single-processor monolithic quantum computers are affected by increased cross talk and difficulty of implementing gates when the number of qubits is in
Externí odkaz:
http://arxiv.org/abs/2407.10769
Autor:
Razavi, Kamran, Fard, Shayan Davari, Karlos, George, Nigade, Vinod, Mühlhäuser, Max, Wang, Lin
The rise of deep learning has led to various successful attempts to apply deep neural networks (DNNs) for important networking tasks such as intrusion detection. Yet, running DNNs in the network control plane, as typically done in existing proposals,
Externí odkaz:
http://arxiv.org/abs/2406.19990
Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real world applications. We propose $\tau$-bench, a benchmark emulating dy
Externí odkaz:
http://arxiv.org/abs/2406.12045
Autor:
Rath, E., Pascua, R., Josaitis, A. T., Ewall-Wice, A., Fagnoni, N., Acedo, E. de Lera, Martinot, Z. E., Abdurashidova, Z., Adams, T., Aguirre, J. E., Baartman, R., Beardsley, A. P., Berkhout, L. M., Bernardi, G., Billings, T. S., Bowman, J. D., Bull, P., Burba, J., Byrne, R., Carey, S., Chen, K. -F., Choudhuri, S., Cox, T., DeBoer, D. R., Dexter, M., Dillon, J. S., Dynes, S., Eksteen, N., Ely, J., Fritz, R., Furlanetto, S. R., Gale-Sides, K., Garsden, H., Gehlot, B. K., Ghosh, A., Gorce, A., Gorthi, D., Halday, Z., Hazelton, B. J., Hewitt, J. N., Hickish, J., Huang, T., Jacobs, D. C., Kern, N. S., Kerrigan, J., Kittiwisit, P., Kolopanis, M., Lanman, A., Liu, A., Ma, Y. -Z., MacMahon, D. H. E., Malan, L., Malgas, C., Malgas, K., Marero, B., McBride, L., Mesinger, A., Mohamed-Hinds, N., Molewa, M., Morales, M. F., Murray, S. G., Nikolic, B., Nuwegeld, H., Parsons, A. R., Patra, N., La Plante, P., Qin, Y., Razavi-Ghods, N., Riley, D., Robnett, J., Rosie, K., Santos, M. G., Sims, P., Singh, S., Storer, D., Swarts, H., Tan, J., Wilensky, M. J., Williams, P. K. G., van Wyngaarden, P., Zheng, H.
Interferometric experiments designed to detect the highly redshifted 21-cm signal from neutral hydrogen are producing increasingly stringent constraints on the 21-cm power spectrum, but some k-modes remain systematics-dominated. Mutual coupling is a
Externí odkaz:
http://arxiv.org/abs/2406.08549
In transportation networks, intersections pose significant risks of collisions due to conflicting movements of vehicles approaching from different directions. To address this issue, various tools can exert influence on traffic safety both directly an
Externí odkaz:
http://arxiv.org/abs/2405.19236