Zobrazeno 1 - 10
of 476
pro vyhledávání: '"Stevens, Rick"'
The objective of drug discovery is to identify chemical compounds that possess specific pharmaceutical properties toward a binding target. Existing large language models (LLMS) can achieve high token matching scores in terms of likelihood for molecul
Externí odkaz:
http://arxiv.org/abs/2406.07025
The aim in many sciences is to understand the mechanisms that underlie the observed distribution of variables, starting from a set of initial hypotheses. Causal discovery allows us to infer mechanisms as sets of cause and effect relationships in a ge
Externí odkaz:
http://arxiv.org/abs/2406.06348
Autor:
Hudson, Nathaniel, Pauloski, J. Gregory, Baughman, Matt, Kamatar, Alok, Sakarvadia, Mansi, Ward, Logan, Chard, Ryan, Bauer, André, Levental, Maksim, Wang, Wenyi, Engler, Will, Skelly, Owen Price, Blaiszik, Ben, Stevens, Rick, Chard, Kyle, Foster, Ian
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM), or models
Externí odkaz:
http://arxiv.org/abs/2402.03480
Autor:
Weber, Maurice, Siebenschuh, Carlo, Butler, Rory, Alexandrov, Anton, Thanner, Valdemar, Tsolakis, Georgios, Jabbar, Haris, Foster, Ian, Li, Bo, Stevens, Rick, Zhang, Ce
We introduce WordScape, a novel pipeline for the creation of cross-disciplinary, multilingual corpora comprising millions of pages with annotations for document layout detection. Relating visual and textual items on document pages has gained further
Externí odkaz:
http://arxiv.org/abs/2312.10188
Autor:
Vasanthakumari, Priyanka, Brettin, Thomas, Zhu, Yitan, Yoo, Hyunseung, Shukla, Maulik, Partin, Alexander, Xia, Fangfang, Narykov, Oleksandr, Stevens, Rick L.
Informed selection of drug candidates for laboratory experimentation provides an efficient means of identifying suitable anti-cancer treatments. The advancement of artificial intelligence has led to the development of computational models to predict
Externí odkaz:
http://arxiv.org/abs/2310.11329
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
While reinforcement learning (RL) has shown promising performance, its sample complexity continues to be a substantial hurdle, restricting its broader application across a variety of domains. Imitation learning (IL) utilizes oracles to improve sample
Externí odkaz:
http://arxiv.org/abs/2310.01737
Autor:
Vescovi, Rafael, Ginsburg, Tobias, Hippe, Kyle, Ozgulbas, Doga, Stone, Casey, Stroka, Abraham, Butler, Rory, Blaiszik, Ben, Brettin, Tom, Chard, Kyle, Hereld, Mark, Ramanathan, Arvind, Stevens, Rick, Vriza, Aikaterini, Xu, Jie, Zhang, Qingteng, Foster, Ian
Advances in robotic automation, high-performance computing (HPC), and artificial intelligence (AI) encourage us to conceive of science factories: large, general-purpose computation- and AI-enabled self-driving laboratories (SDLs) with the generality
Externí odkaz:
http://arxiv.org/abs/2308.09793
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
Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits - e.g. differences in cell function, disease, drug resistance and others. Causal learners based on graphical models rely on interventional
Externí odkaz:
http://arxiv.org/abs/2304.03210