activity
20222024
most citedPure Transformers are Powerful Graph Learners

57 citations · 60 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AR20241 cited

AERO: Adaptive Erase Operation for Improving Lifetime and Performance of Modern NAND Flash-Based SSDs

Sungjun Cho, Beomjun Kim, Hyunuk Cho +4

This work investigates a new erase scheme in NAND flash memory to improve the lifetime and performance of modern solid-state drives (SSDs). In NAND flash memory, an erase operation…

cs.AR2024

Block-SSD: A New Block-Based Blocking SSD Architecture

Ryan Wong, Arjun Tyagi, Sungjun Cho +2

Computer science and related fields (e.g., computer engineering, computer hardware engineering, electrical engineering, electrical and computer engineering, computer systems engine…

cs.CV20241 cited

Learning Equi-angular Representations for Online Continual Learning

Minhyuk Seo, Hyunseo Koh, Wonje Jeung +7

Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e.g., single-epoch training). To address the challenge, we prop…

cs.LG2023

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…

cs.LG20231 cited

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…

cs.LG2022

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…