| Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network YC Kim, KR Kim, YH Choe Computer methods and programs in biomedicine 185, 105150, 2020 | 29 | 2020 |
| EVCMR: a tool for the quantitative evaluation and visualization of cardiac MRI data YC Kim, KR Kim, K Choi, M Kim, Y Chung, YH Choe Computers in Biology and Medicine 111, 103334, 2019 | 13 | 2019 |
| A probabilistic machine learning approach to scheduling parallel loops with Bayesian optimization KR Kim, Y Kim, S Park IEEE Transactions on Parallel and Distributed Systems 32 (7), 1815-1827, 2020 | 12 | 2020 |
| Markov chain score ascent: A unifying framework of variational inference with Markovian gradients K Kim, J Oh, JR Gardner, AB Dieng, H Kim Neural Information Processing Systems 35, 34802-34816, 2022 | 8* | 2022 |
| Fast calculation software for modified Look-Locker inversion recovery (MOLLI) T1 mapping YC Kim, KR Kim, H Lee, YH Choe BMC Medical Imaging 21, 1-10, 2021 | 7 | 2021 |
| On the Convergence of Black-Box Variational Inference K Kim, J Oh, K Wu, Y Ma, JR Gardner Neural Information Processing Systems 36, 44615-44657, 2023 | 6* | 2023 |
| Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference K Kim, K Wu, J Oh, JR Gardner International Conference on Machine Learning 202, 16853-16876, 2023 | 5 | 2023 |
| A GPU scheduling framework to accelerate hyper-parameter optimization in deep learning clusters J Son, Y Yoo, K Kim, Y Kim, K Lee, S Park Electronics 10 (3), 350, 2021 | 5 | 2021 |
| The Behavior and Convergence of Local Bayesian Optimization K Wu, K Kim, R Garnett, JR Gardner Neural Information Processing Systems 36, 73497-73523, 2023 | 3 | 2023 |
| Evaluating the strong scalability of parallel Markov-chain Monte Carlo algorithms K Kim, S Maskell, S Park | 1 | 2020 |
| Towards robust data-driven parallel loop scheduling using Bayesian optimization K Kim, Y Kim, S Park 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation …, 2019 | 1 | 2019 |
| Stochastic Approximation with Biased MCMC for Expectation Maximization S Gruffaz, K Kim, AO Durmus, JR Gardner International Conference on Artificial Intelligence and Statistics, 2024 | | 2024 |
| Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? K Kim, Y Ma, JR Gardner International Conference on Artificial Intelligence and Statistics, 2024 | | 2024 |
| Provably Scalable Black-Box Variational Inference with Structured Variational Families J Ko, K Kim, WC Kim, JR Gardner arXiv preprint arXiv:2401.10989, 2024 | | 2024 |
| Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound K Kim, S Maskell, JF Ralph arXiv preprint arXiv:2212.03824, 2022 | | 2022 |
Jacob GardnerUniversity of PennsylvaniaVerified email at seas.upenn.edu
