57 citations · 71 across the 14 of their papers we have counts for
7 papers · 1 filter
Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs
Sungmin Cha, Sungjun Cho, Dasol Hwang +1
Large Language Models (LLMs) have demonstrated strong reasoning and memorization capabilities via pretraining on massive textual corpora. However, this poses risk of privacy and co…
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning
Sungjun Cho, Seunghyuk Cho, Sungwoo Park +3
Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space. While there exist graph neural networks that leverage hyperb…
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
Sungjun Cho, Dae-Woong Jeong, Sung Moon Ko +5
Pretraining molecular representations from large unlabeled data is essential for molecular property prediction due to the high cost of obtaining ground-truth labels. While there ex…
Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers
Sungmin Cha, Sungjun Cho, Dasol Hwang +3
Since the recent advent of regulations for data protection (e.g., the General Data Protection Regulation), there has been increasing demand in deleting information learned from sen…
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
Sungjun Cho, Seonwoo Min, Jinwoo Kim +3
To overcome the quadratic cost of self-attention, recent works have proposed various sparse attention modules, most of which fall under one of two groups: 1) sparse attention under…
Equivariant Hypergraph Neural Networks
Jinwoo Kim, Saeyoon Oh, Sungjun Cho +1
Many problems in computer vision and machine learning can be cast as learning on hypergraphs that represent higher-order relations. Recent approaches for hypergraph learning extend…
Pure Transformers are Powerful Graph Learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min +4
We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat…