Zobrazeno 1 - 6
of 6
pro vyhledávání: '"John Kevin Cava"'
Publikováno v:
IEEE Transactions on Information Theory. 68:6021-6051
We introduce a tunable loss function called $\alpha$-loss, parameterized by $\alpha \in (0,\infty]$, which interpolates between the exponential loss ($\alpha = 1/2$), the log-loss ($\alpha = 1$), and the 0-1 loss ($\alpha = \infty$), for the machine
Autor:
Nicholas Ho, John Kevin Cava, John Vant, Ankita Shukla, Jake Miratsky, Pavan Turaga, Ross Maciejewski, Abhishek Singharoy
In this paper, we develop a formulation to utilize reinforcement learning and sampling-based robotics planning to derive low free energy transition pathways between two known states. Our formulation uses Jarzynski’s equality and the stiffspring app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::51fec0f82d68126af5a08c8aa2221017
https://doi.org/10.1101/2022.10.04.510845
https://doi.org/10.1101/2022.10.04.510845
Publikováno v:
Cancer Research. 83:5376-5376
The ability to accurately identify peptide ligands for a given major histocompatibility complex class I (MHC-I) molecule has immense value for targeted anticancer therapeutics. However, the highly polymorphic nature of the MHC-I protein makes univers
Publikováno v:
ICCV Workshops
Approximating the distance of objects present in an image remains an important problem for computer vision applications. Current SOTA methods rely on formulating this problem to convenience depth estimation at every pixel; however, there are limitati
Autor:
John Kevin Cava, Vant, John W., Nicholas Ho, Ankita Shukla, Turaga, Pavan K., Ross Maciejewski, Abhishek Singharoy
Publikováno v:
Ankita Shukla
In this paper, we utilized generative models, and reformulate it for problems in molecular dynamics (MD) simulation, by introducing an MD potential energy component to our generative model. By incorporating potential energy as calculated from TorchMD
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2fb1974cd56704363a896f3bd785bd8f
https://arxiv.org/abs/2111.14053
https://arxiv.org/abs/2111.14053