Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models. - Keunseo Kim; Juncheol Shin; Heeyoung Kim (NeurIPS, 2021)

NIPQ: Noise Injection Pseudo Quantization for Automated DNN Optimization. - Sein Park; Junhyuk So; Juncheol Shin; Eunhyeok Park (CoRR, 2022)

A Case Study of Quantizing Convolutional Neural Networks for Fast Disease Diagnosis on Portable Medical Devices. - Mukhammed Garifulla; Juncheol Shin; Chanho Kim; Won Hwa Kim; Hye Jung Kim; Jaeil Kim; Seokin Hong (Sensors, 2022)

NIPQ: Noise proxy-based Integrated Pseudo-Quantization. - Juncheol Shin; Junhyuk So; Sein Park; Seungyeop Kang; Sungjoo Yoo; Eunhyeok Park (CVPR, 2023)

HOT: Hadamard-based Optimized Training. - Seonggon Kim; Juncheol Shin; Seung-taek Woo; Eunhyeok Park (CVPR, 2025)

Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation. - Juncheol Shin; Minsang Seok; Seonggon Kim; Eunhyeok Park (CoRR, 2025)