Search Results for author: Ji Won Park

Found 12 papers, 4 papers with code

Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic Prediction

no code implementations2 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.

Graph Neural Network

Blind Biological Sequence Denoising with Self-Supervised Set Learning

no code implementations4 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.

Denoising

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks

1 code implementation1 Jun 2023 Ji Won Park, Nataša Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho

Motivated by this link, we propose the Pareto-compliant CDF indicator and the associated acquisition function, BOtied.

Bayesian Optimization

Chain of Log-Concave Markov Chains

no code implementations31 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.

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks

1 code implementation15 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.

Graph Neural Network

Multi-segment preserving sampling for deep manifold sampler

no code implementations9 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.

Language Modelling

Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant

2 code implementations30 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.

Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing

1 code implementation26 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.

Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations

no code implementations12 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.

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