7 papers
LatentTune: Efficient Tuning of High Dimensional Database Parameters via Latent Representation Learning
Sein Kwon, Youngwan Jo, Seungyeon Choi +3
As data volumes continue to grow, optimizing database performance has become increasingly critical, making the implementation of effective tuning methods essential. Among various a…
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
Sunghyun Ahn, Youngwan Jo, Kijung Lee +3
Video anomaly detection (VAD) is crucial for video analysis and surveillance in computer vision. However, existing VAD models rely on learned normal patterns, which makes them diff…
Relation-Aware Bayesian Optimization of DBMS Configurations Guided by Affinity Scores
Sein Kwon, Seulgi Baek, Hyunseo Yang +2
Database Management Systems (DBMSs) are fundamental for managing large-scale and heterogeneous data, and their performance is critically influenced by configuration parameters. Eff…
GranQ: Efficient Channel-wise Quantization via Vectorized Pre-Scaling for Zero-Shot QAT
Inpyo Hong, Youngwan Jo, Hyojeong Lee +3
Zero-shot quantization (ZSQ) enables neural network compression without original training data, making it a promising solution for restricted data access scenarios. To compensate f…
Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge Computing
Inpyo Hong, Youngwan Jo, Hyojeong Lee +2
We introduce AKT (Advanced Knowledge Transfer), a novel method to enhance the training ability of low-bit quantized (Q) models in the field of zero-shot quantization (ZSQ). Existin…
MRNet: Multifaceted Resilient Networks for Medical Image-to-Image Translation
Hyojeong Lee, Youngwan Jo, Inpyo Hong +1
We propose a Multifaceted Resilient Network(MRNet), a novel architecture developed for medical image-to-image translation that outperforms state-of-the-art methods in MRI-to-CT and…