2 citations · 2 across the 9 of their papers we have counts for
9 papers
Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor
Jiyeon Kim, Byungju Lee, Won-Yong Shin
Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making their fast and accurate predicti…
Semi-Supervised Neural Super-Resolution for Mesh-Based Simulations
Jiyeon Kim, Youngjoon Hong, Won-Yong Shin
Mesh-based simulations provide high-fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine meshes, leading to substantial…
Real-time prediction of breast cancer sites using deformation-aware graph neural network
Kyunghyun Lee, Yong-Min Shin, Minwoo Shin +4
Early diagnosis of breast cancer is crucial, enabling the establishment of appropriate treatment plans and markedly enhancing patient prognosis. While direct magnetic resonance ima…
On the Feasibility of Fidelity for Graph Pruning
Yong-Min Shin, Won-Yong Shin
As one of popular quantitative metrics to assess the quality of explanation of graph neural networks (GNNs), fidelity measures the output difference after removing unimportant part…
Faithful and Accurate Self-Attention Attribution for Message Passing Neural Networks via the Computation Tree Viewpoint
Yong-Min Shin, Siqing Li, Xin Cao +1
The self-attention mechanism has been adopted in various popular message passing neural networks (MPNNs), enabling the model to adaptively control the amount of information that fl…
Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
Jin-Duk Park, Yong-Min Shin, Won-Yong Shin
A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art performance on the recommendation accuracy by using a low-pass filter (LPF) without a…
Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
Yu Hou, Jin-Duk Park, Won-Yong Shin
A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature. H…
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs
Yong-Min Shin, Won-Yong Shin
Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semisupervised node classification on graphs, by training a student MLP by knowledge distillation from a…
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs
Yong-Min Shin, Won-Yong Shin
Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semi-supervised node classification on graphs, by training a student MLP by knowledge distillation (KD) f…