Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Tseng, Alex M"'
Deep neural networks excel in mapping genomic DNA sequences to associated readouts (e.g., protein-DNA binding). Beyond prediction, the goal of these networks is to reveal to scientists the underlying motifs (and their syntax) which drive genome regul
Externí odkaz:
http://arxiv.org/abs/2410.06211
Autor:
Uehara, Masatoshi, Zhao, Yulai, Black, Kevin, Hajiramezanali, Ehsan, Scalia, Gabriele, Diamant, Nathaniel Lee, Tseng, Alex M, Levine, Sergey, Biancalani, Tommaso
Diffusion models excel at modeling complex data distributions, including those of images, proteins, and small molecules. However, in many cases, our goal is to model parts of the distribution that maximize certain properties: for example, we may want
Externí odkaz:
http://arxiv.org/abs/2402.16359
Autor:
Uehara, Masatoshi, Zhao, Yulai, Black, Kevin, Hajiramezanali, Ehsan, Scalia, Gabriele, Diamant, Nathaniel Lee, Tseng, Alex M, Biancalani, Tommaso, Levine, Sergey
Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins. While diffusion models are trained to represent the distribution in the training dataset, we often are more concerned with other properties,
Externí odkaz:
http://arxiv.org/abs/2402.15194
Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos. Despite their success, there has been limited research on how the underlying diffusion process and the final c
Externí odkaz:
http://arxiv.org/abs/2306.02957
Diffusion models achieve state-of-the-art performance in generating realistic objects and have been successfully applied to images, text, and videos. Recent work has shown that diffusion can also be defined on graphs, including graph representations
Externí odkaz:
http://arxiv.org/abs/2302.03790
Autor:
Diamant, Nathaniel, Tseng, Alex M., Chuang, Kangway V., Biancalani, Tommaso, Scalia, Gabriele
Deep graph generative modeling has proven capable of learning the distribution of complex, multi-scale structures characterizing real-world graphs. However, one of the main limitations of existing methods is their large output space, which limits gen
Externí odkaz:
http://arxiv.org/abs/2301.10857
Hierarchically branched diffusion models leverage dataset structure for class-conditional generation
Class-labeled datasets, particularly those common in scientific domains, are rife with internal structure, yet current class-conditional diffusion models ignore these relationships and implicitly diffuse on all classes in a flat fashion. To leverage
Externí odkaz:
http://arxiv.org/abs/2212.10777
Autor:
Ludwig, Leif S. *, *, Lareau, Caleb A. *, Bao, Erik L., Liu, Nan, Utsugisawa, Taiju, Tseng, Alex M., Myers, Samuel A., Verboon, Jeffrey M., Ulirsch, Jacob C., Luo, Wendy, Muus, Christoph, Fiorini, Claudia, Olive, Meagan E., Vockley, Christopher M., Munschauer, Mathias, Hunter, Abigail, Ogura, Hiromi, Yamamoto, Toshiyuki, Inada, Hiroko, Nakagawa, Shinichiro, Ohzono, Shuichi, Subramanian, Vidya, Chiarle, Roberto, Glader, Bertil, Carr, Steven A., Aryee, Martin J., Kundaje, Anshul, Orkin, Stuart H., Regev, Aviv, McCavit, Timothy L., Kanno, Hitoshi, Sankaran, Vijay G. *
Publikováno v:
In Blood 21 April 2022 139(16):2534-2546
Autor:
Heavner, Whitney E., Ji, Shaoyi, Notwell, James H., Dyer, Ethan S., Tseng, Alex M., Birgmeier, Johannes, Yoo, Boyoung, Bejerano, Gill, McConnell, Susan K.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Oct 01. 117(40), 25074-25084.
Externí odkaz:
https://www.jstor.org/stable/26969478
Autor:
Tseng, Alex M.1 (AUTHOR), Harish Bindiganavile, Shruthi2 (AUTHOR), Bhat, Nita2 (AUTHOR), Divatia, Mukul K.3 (AUTHOR), Lee, Andrew G.1,2,4,5,6,7 (AUTHOR) aglee@houstonmethodist.org
Publikováno v:
Neuro-Ophthalmology. Oct2021, Vol. 45 Issue 5, p329-333. 5p.