12 citations · 22 across the 11 of their papers we have counts for
11 papers
Rethinking the Test-Time Prompt Tuning Objective from the Perspective of Calibration
Jungwon Choi, Hyeonseo Jang, Kibok Lee +1
Test-time prompt tuning (TPT) has emerged as a powerful paradigm, refining prompts for each test sample via entropy minimization (EM) over multiple augmented views. However, we ide…
Are Compact Rationales Free? Measuring Tile Selection Headroom in Frozen WSI-MIL
Hyun Do Jung, Jungwon Choi, Soojung Choi +2
Whole-slide image (WSI) multiple instance learning (MIL) classifiers can achieve strong slide-level AUC while leaving the full-bag prediction opaque. Attention scores are widely re…
Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models
Mingyeong Kim, Jungwon Choi, Chaeyun Jang +1
Vision-language models (VLMs) are often deployed on text-only inputs, although they are trained with images. We find that removing the vision modality causes large drops in accurac…
ReaMIL: Reasoning- and Evidence-Aware Multiple Instance Learning for Whole-Slide Histopathology
Hyun Do Jung, Jungwon Choi, Hwiyoung Kim
We introduce ReaMIL (Reasoning- and Evidence-Aware MIL), a multiple instance learning approach for whole-slide histopathology that adds a light selection head to a strong MIL backb…
FOSSIL: Regret-Minimizing Curriculum Learning for Metadata-Free and Low-Data Mpox Diagnosis
Sahng-Min Han, Minjae Kim, Jinho Cha +4
Deep learning in small and imbalanced biomedical datasets remains fundamentally constrained by unstable optimization and poor generalization. We present the first biomedical implem…
Stochastic Optimal Control for Continuous-Time fMRI Representation Learning
Joonhyeong Park, Byoungwoo Park, Chang-Bae Bang +4
Learning robust representations from functional magnetic resonance imaging (fMRI) is fundamentally challenged by the temporal irregularity and noise inherent in data from heterogen…
Stochastic Optimal Control for Diffusion Bridges in Function Spaces
Byoungwoo Park, Jungwon Choi, Sungbin Lim +1
Recent advancements in diffusion models and diffusion bridges primarily focus on finite-dimensional spaces, yet many real-world problems necessitate operations in infinite-dimensio…
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain
Jungwon Choi, Hyungi Lee, Byung-Hoon Kim +1
Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks. However, obtaining extensive la…
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
Byung-Hoon Kim, Jungwon Choi, EungGu Yun +3
Graph Transformers have recently been successful in various graph representation learning tasks, providing a number of advantages over message-passing Graph Neural Networks. Utiliz…
A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data
Jungwon Choi, Seongho Keum, EungGu Yun +2
Deep neural networks trained on Functional Connectivity (FC) networks extracted from functional Magnetic Resonance Imaging (fMRI) data have gained popularity due to the increasing…
Convergence Analysis of Optimization Algorithms
HyoungSeok Kim, JiHoon Kang, WooMyoung Park +5
The regret bound of an optimization algorithms is one of the basic criteria for evaluating the performance of the given algorithm. By inspecting the differences between the regret…