Search Results for author: JungWon Choi

Found 5 papers, 1 papers with code

Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain

no code implementations11 Mar 2024 JungWon Choi, Hyungi Lee, Byung-Hoon Kim, Juho Lee

Although generative self-supervised learning techniques, especially masked autoencoders, have shown promising results in representation learning in various domains, their application to dynamic graphs for dynamic functional connectivity remains underexplored, facing challenges in capturing high-level semantic representations.

Representation Learning Self-Supervised Learning

A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data

no code implementations4 Dec 2023 JungWon Choi, Seongho Keum, Eunggu Yun, Byung-Hoon Kim, Juho Lee

Deep neural networks trained on Functional Connectivity (FC) networks extracted from functional Magnetic Resonance Imaging (fMRI) data have gained popularity due to the increasing availability of data and advances in model architectures, including Graph Neural Network (GNN).

Self-Supervised Learning

On Divergence Measures for Bayesian Pseudocoresets

1 code implementation12 Oct 2022 Balhae Kim, JungWon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee

Finally, we propose a novel Bayesian pseudocoreset algorithm based on minimizing forward KL divergence.

Bayesian Inference Image Classification

Convergence Analysis of Optimization Algorithms

no code implementations6 Jul 2017 HyoungSeok Kim, JiHoon Kang, WooMyoung Park, SukHyun Ko, YoonHo Cho, DaeSung Yu, YoungSook Song, JungWon Choi

The regret bound of an optimization algorithms is one of the basic criteria for evaluating the performance of the given algorithm.

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