From the 1 of 10 linked papers with an AI index.
10 papers
Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models
Hyunho Lee, Kyomin Hwang, Hyeonjin Kim +3
The paper proposes GoodQ, a method that uses off-the-shelf generative models to create synthetic training data for zero-shot quantization of object detectors, enabling low-bit quan…
Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics
Kyomin Hwang, Hyeonjin Kim, Hyunho Lee +1
Recent advances in Large Language Models (LLMs) have motivated their adoption across a wide range of domains, including Artificial Intelligence (AI) for mental health. Given the gr…
ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
Suyoung Kim, Sunghyun Wee, Hyeonjin Kim +3
Rotation-based Post-Training Quantization (PTQ) has emerged as a promising solution for mitigating activation outliers in the quantization of Large Language Models (LLMs). Global r…
Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation
Kyomin Hwang, Hyeonjin Kim, Sangyeon Cho +1
While LLMs are increasingly used in commercial services, they pose privacy risks such as leakage of sensitive personally identifiable information (PII). For LLMs trained on multili…
Retrieval-Augmented Generation Based Nurse Observation Extraction
Kyomin Hwang, Nojun Kwak
Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into th…
Safety-Preserving PTQ via Contrastive Alignment Loss
Sunghyun Wee, Suyoung Kim, Hyeonjin Kim +2
Post-Training Quantization (PTQ) has become the de-facto standard for efficient LLM deployment, yet its optimization objective remains fundamentally incomplete. Standard PTQ method…