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Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
Hyunjin Seo, Kyusung Seo, Joonhyung Park +1
Recent advancements in graph neural networks (GNNs) have highlighted the critical need of calibrating model predictions, with neighborhood prediction similarity recognized as a piv…
REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring
Taewon Kim, Hyunjin Seo, Sungsoo Ahn +1
Predicting the ground-state 3D molecular conformations from 2D molecular graphs is critical in computational chemistry due to its profound impact on molecular properties. Deep lear…
PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
Yeonsung Jung, Heecheol Yun, Joonhyung Park +2
Neural Radiance Fields (NeRF) have shown remarkable performance in learning 3D scenes. However, NeRF exhibits vulnerability when confronted with distractors in the training images…
TEDDY: Trimming Edges with Degree-based Discrimination strategY
Hyunjin Seo, Jihun Yun, Eunho Yang
Since the pioneering work on the lottery ticket hypothesis for graph neural networks (GNNs) was proposed in Chen et al. (2021), the study on finding graph lottery tickets (GLT) has…