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Hongil Kim

2 accepted papers

2026

Universal Compressed Image Restoration via Codec-Aware Conditioning with Reinforcement Learning

AAAI 2026technical

We address the task of universal compressed image restoration, which involves recovering high-quality images degraded by a wide range of codecs and compression levels. While prior methods have made significant progress, they typically target specific degradation types and struggle to generalize acro

Cited by 0SourcePDFScholar
2024

VITA: ‘Carefully Chosen and Weighted Less’ Is Better in Medication Recommendation

AAAI 2024technical

We address the medication recommendation problem, which aims to recommend effective medications for a patient's current visit by utilizing information (e.g., diagnoses and procedures) given at the patient's current and past visits. While there exist a number of recommender systems designed for this…