57 citations · 147 across the 34 of their papers we have counts for
6 papers · 2 filters
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
Sungik Choi, Hankook Lee, Honglak Lee +1
Novelty detection is a fundamental task of machine learning which aims to detect abnormal ( out-of-distribution (OOD)) samples. Since diffusion models have recently…
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…
Multimodal Subtask Graph Generation from Instructional Videos
Yunseok Jang, Sungryull Sohn, Lajanugen Logeswaran +3
Real-world tasks consist of multiple inter-dependent subtasks (e.g., a dirty pan needs to be washed before it can be used for cooking). In this work, we aim to model the causal dep…
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching
Byoungjip Kim, Sungik Choi, Dasol Hwang +2
Despite surprising performance on zero-shot transfer, pre-training a large-scale multimodal model is often prohibitive as it requires a huge amount of data and computing resources.…
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…