Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Ren, Yihui"'
Autor:
Zhu, Feiqin, Torbunov, Dmitrii, Ren, Yihui, Jiang, Zhongjing, Zhao, Tianqiao, Yogarathnam, Amirthagunaraj, Yue, Meng
Data-driven modeling for dynamic systems has gained widespread attention in recent years. Its inverse formulation, parameter estimation, aims to infer the inherent model parameters from observations. However, parameter degeneracy, where different com
Externí odkaz:
http://arxiv.org/abs/2411.10431
Autor:
Park, David K., Ren, Yihui, Kilic, Ozgur O., Korchuganova, Tatiana, Vatsavai, Sairam Sri, Boudreau, Joseph, Chowdhury, Tasnuva, Feng, Shengyu, Khan, Raees, Kim, Jaehyung, Klasky, Scott, Maeno, Tadashi, Nilsson, Paul, Outschoorn, Verena Ingrid Martinez, Podhorszki, Norbert, Suter, Frederic, Yang, Wei, Yang, Yiming, Yoo, Shinjae, Klimentov, Alexei, Hoisie, Adolfy
Large-scale international scientific collaborations, such as ATLAS, Belle II, CMS, and DUNE, generate vast volumes of data. These experiments necessitate substantial computational power for varied tasks, including structured data processing, Monte Ca
Externí odkaz:
http://arxiv.org/abs/2410.07940
Autor:
Kharel, Shubha R., Mukim, Prashansa, Maj, Piotr, Deptuch, Grzegorz W., Yoo, Shinjae, Ren, Yihui, Mandal, Soumyajit
Extreme edge-AI systems, such as those in readout ASICs for radiation detection, must operate under stringent hardware constraints such as micron-level dimensions, sub-milliwatt power, and nanosecond-scale speed while providing clear accuracy advanta
Externí odkaz:
http://arxiv.org/abs/2407.14560
Autor:
Go, Yeonju, Torbunov, Dmitrii, Rinn, Timothy, Huang, Yi, Yu, Haiwang, Viren, Brett, Lin, Meifeng, Ren, Yihui, Huang, Jin
Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. Howe
Externí odkaz:
http://arxiv.org/abs/2406.01602
Autor:
Xu, Wei, DeSantis, Derek Freeman, Luo, Xihaier, Parmar, Avish, Tan, Klaus, Nadiga, Balu, Ren, Yihui, Yoo, Shinjae
Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present a novel model called MMGN (Multiplicative and Modulated Gabor Network
Externí odkaz:
http://arxiv.org/abs/2404.06418
Reliably reconstructing physical fields from sparse sensor data is a challenge that frequently arises in many scientific domains. In practice, the process generating the data often is not understood to sufficient accuracy. Therefore, there is a growi
Externí odkaz:
http://arxiv.org/abs/2401.11611
High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high-energy physics. Developing real-time data compression algorithms to reduce such data at h
Externí odkaz:
http://arxiv.org/abs/2310.15026
Autor:
Song, Shuaiwen Leon, Kruft, Bonnie, Zhang, Minjia, Li, Conglong, Chen, Shiyang, Zhang, Chengming, Tanaka, Masahiro, Wu, Xiaoxia, Rasley, Jeff, Awan, Ammar Ahmad, Holmes, Connor, Cai, Martin, Ghanem, Adam, Zhou, Zhongzhu, He, Yuxiong, Luferenko, Pete, Kumar, Divya, Weyn, Jonathan, Zhang, Ruixiong, Klocek, Sylwester, Vragov, Volodymyr, AlQuraishi, Mohammed, Ahdritz, Gustaf, Floristean, Christina, Negri, Cristina, Kotamarthi, Rao, Vishwanath, Venkatram, Ramanathan, Arvind, Foreman, Sam, Hippe, Kyle, Arcomano, Troy, Maulik, Romit, Zvyagin, Maxim, Brace, Alexander, Zhang, Bin, Bohorquez, Cindy Orozco, Clyde, Austin, Kale, Bharat, Perez-Rivera, Danilo, Ma, Heng, Mann, Carla M., Irvin, Michael, Pauloski, J. Gregory, Ward, Logan, Hayot, Valerie, Emani, Murali, Xie, Zhen, Lin, Diangen, Shukla, Maulik, Foster, Ian, Davis, James J., Papka, Michael E., Brettin, Thomas, Balaprakash, Prasanna, Tourassi, Gina, Gounley, John, Hanson, Heidi, Potok, Thomas E, Pasini, Massimiliano Lupo, Evans, Kate, Lu, Dan, Lunga, Dalton, Yin, Junqi, Dash, Sajal, Wang, Feiyi, Shankar, Mallikarjun, Lyngaas, Isaac, Wang, Xiao, Cong, Guojing, Zhang, Pei, Fan, Ming, Liu, Siyan, Hoisie, Adolfy, Yoo, Shinjae, Ren, Yihui, Tang, William, Felker, Kyle, Svyatkovskiy, Alexey, Liu, Hang, Aji, Ashwin, Dalton, Angela, Schulte, Michael, Schulz, Karl, Deng, Yuntian, Nie, Weili, Romero, Josh, Dallago, Christian, Vahdat, Arash, Xiao, Chaowei, Gibbs, Thomas, Anandkumar, Anima, Stevens, Rick
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors fro
Externí odkaz:
http://arxiv.org/abs/2310.04610
Background: Customers find great psychological satisfaction and pleasure when shopping. Customer satisfaction is crucial for a business's success, and increasing it will strengthenfinancial performance and competitiveness. Offline shopping still domi
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-121301
Autor:
Chen, Wei, Ren, Yihui, Kagawa, Ai, Carbone, Matthew R., Chen, Samuel Yen-Chi, Qu, Xiaohui, Yoo, Shinjae, Clyde, Austin, Ramanathan, Arvind, Stevens, Rick L., van Dam, Hubertus J. J., Lu, Deyu
Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidel
Externí odkaz:
http://arxiv.org/abs/2308.01921