Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Dieleman, Sander"'
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:
Baird, Alice, Manzelli, Rachel, Tzirakis, Panagiotis, Gagne, Chris, Li, Haoqi, Allen, Sadie, Dieleman, Sander, Kulis, Brian, Narayanan, Shrikanth S., Cowen, Alan
The NeurIPS 2023 Machine Learning for Audio Workshop brings together machine learning (ML) experts from various audio domains. There are several valuable audio-driven ML tasks, from speech emotion recognition to audio event detection, but the communi
Externí odkaz:
http://arxiv.org/abs/2403.14048
Autor:
Du, Yilun, Durkan, Conor, Strudel, Robin, Tenenbaum, Joshua B., Dieleman, Sander, Fergus, Rob, Sohl-Dickstein, Jascha, Doucet, Arnaud, Grathwohl, Will
Since their introduction, diffusion models have quickly become the prevailing approach to generative modeling in many domains. They can be interpreted as learning the gradients of a time-varying sequence of log-probability density functions. This int
Externí odkaz:
http://arxiv.org/abs/2302.11552
Autor:
Dieleman, Sander, Sartran, Laurent, Roshannai, Arman, Savinov, Nikolay, Ganin, Yaroslav, Richemond, Pierre H., Doucet, Arnaud, Strudel, Robin, Dyer, Chris, Durkan, Conor, Hawthorne, Curtis, Leblond, Rémi, Grathwohl, Will, Adler, Jonas
Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement. Their success hinges on the fact that the underlying physical phenomena are continuous. Fo
Externí odkaz:
http://arxiv.org/abs/2211.15089
Autor:
Strudel, Robin, Tallec, Corentin, Altché, Florent, Du, Yilun, Ganin, Yaroslav, Mensch, Arthur, Grathwohl, Will, Savinov, Nikolay, Dieleman, Sander, Sifre, Laurent, Leblond, Rémi
Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can simply project tokens in a continuous space of embeddings, as is standar
Externí odkaz:
http://arxiv.org/abs/2211.04236
Diffusion models typically operate in the standard framework of generative modelling by producing continuously-valued datapoints. To this end, they rely on a progressive Gaussian smoothing of the original data distribution, which admits an SDE interp
Externí odkaz:
http://arxiv.org/abs/2210.14784
Autor:
Hawthorne, Curtis, Jaegle, Andrew, Cangea, Cătălina, Borgeaud, Sebastian, Nash, Charlie, Malinowski, Mateusz, Dieleman, Sander, Vinyals, Oriol, Botvinick, Matthew, Simon, Ian, Sheahan, Hannah, Zeghidour, Neil, Alayrac, Jean-Baptiste, Carreira, João, Engel, Jesse
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohibitively expensive t
Externí odkaz:
http://arxiv.org/abs/2202.07765
Autor:
Wang, Luyu, Luc, Pauline, Wu, Yan, Recasens, Adria, Smaira, Lucas, Brock, Andrew, Jaegle, Andrew, Alayrac, Jean-Baptiste, Dieleman, Sander, Carreira, Joao, Oord, Aaron van den
The ability to learn universal audio representations that can solve diverse speech, music, and environment tasks can spur many applications that require general sound content understanding. In this work, we introduce a holistic audio representation e
Externí odkaz:
http://arxiv.org/abs/2111.12124
Semantically meaningful information content in perceptual signals is usually unevenly distributed. In speech signals for example, there are often many silences, and the speed of pronunciation can vary considerably. In this work, we propose slow autoe
Externí odkaz:
http://arxiv.org/abs/2103.06089
The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as
Externí odkaz:
http://arxiv.org/abs/2103.03841