no code implementations • 2 Oct 2023 • Omid Bazgir, Zichen Wang, Ji Won Park, Marc Hafner, James Lu
Additionally, we show that the graph encoder is able to effectively utilize multimodal data to enhance tumor predictions.
no code implementations • 4 Sep 2023 • Nathan Ng, Ji Won Park, Jae Hyeon Lee, Ryan Lewis Kelly, Stephen Ra, Kyunghyun Cho
This set embedding represents the "average" of the subreads and can be decoded into a prediction of the clean sequence.
no code implementations • 1 Jun 2023 • Ji Won Park, Nataša Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho
At the heart of MOBO is the acquisition function, which determines the next candidate to evaluate by navigating the best compromises among the objectives.
no code implementations • 31 May 2023 • Saeed Saremi, Ji Won Park, Francis Bach
We introduce a theoretical framework for sampling from unnormalized densities based on a smoothing scheme that uses an isotropic Gaussian kernel with a single fixed noise scale.
no code implementations • 15 Feb 2023 • Michael Maser, Ji Won Park, Joshua Yao-Yu Lin, Jae Hyeon Lee, Nathan C. Frey, Andrew Watkins
We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers.
1 code implementation • 15 Nov 2022 • Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman, The LSST Dark Energy Science Collaboration
For each test set of 1, 000 sightlines, the BGNN infers the individual $\kappa$ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population.
no code implementations • 8 Oct 2022 • Ji Won Park, Samuel Stanton, Saeed Saremi, Andrew Watkins, Henri Dwyer, Vladimir Gligorijevic, Richard Bonneau, Stephen Ra, Kyunghyun Cho
Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences.
no code implementations • 9 May 2022 • Daniel Berenberg, Jae Hyeon Lee, Simon Kelow, Ji Won Park, Andrew Watkins, Vladimir Gligorijević, Richard Bonneau, Stephen Ra, Kyunghyun Cho
We introduce an alternative approach to this guided sampling procedure, multi-segment preserving sampling, that enables the direct inclusion of domain-specific knowledge by designating preserved and non-preserved segments along the input sequence, thereby restricting variation to only select regions.
no code implementations • 2 Jun 2021 • Ji Won Park, Ashley Villar, Yin Li, Yan-Fei Jiang, Shirley Ho, Joshua Yao-Yu Lin, Philip J. Marshall, Aaron Roodman
Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter.
2 code implementations • 30 Nov 2020 • Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin, Aaron Roodman
The computation time for the entire pipeline -- including the training set generation, BNN training, and $H_0$ inference -- translates to 9 minutes per lens on average for 200 lenses and converges to 6 minutes per lens as the sample size is increased.
1 code implementation • 26 Oct 2020 • Sebastian Wagner-Carena, Ji Won Park, Simon Birrer, Philip J. Marshall, Aaron Roodman, Risa H. Wechsler
We show that the posterior PDFs are sufficiently accurate (i. e., statistically consistent with the truth) across a wide variety of power-law elliptical lens mass distributions.
no code implementations • 12 Aug 2020 • Yonghyun Nam, Jae-Seung Yun, Seung Mi Lee, Ji Won Park, Ziqi Chen, Brian Lee, Anurag Verma, Xia Ning, Li Shen, Dokyoon Kim
To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs.