NP-CGRA: Extending CGRAs for Efficient Processing of Light-weight Deep Neural Networks. - Jungi Lee 001; Jongeun Lee (DATE, 2021)

Extension Of Guided Filter With Memory For Projection On Training Spectrum. - Myounghwan Kim; Jungi Lee 001 (WHISPERS, 2022)

Hyperspectral Image Visualization Through Neural Network For The Food Industry. - Hyeok Yoon; Jungi Lee 001 (WHISPERS, 2022)

Specializing CGRAs for Light-Weight Convolutional Neural Networks. - Jungi Lee 001; Jongeun Lee (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022)

FastSimiFeat: A Fast and Generalized Approach Utilizing k-NN for Noisy Data Handling. - Jungi Lee 001; Hwiwoo Park; Myounghwan Kim; Jiseong Yoon; Kwangsun Yoo; Seok-Joo Byun (CIKM, 2024)

PA2E: Real-Time Anomaly Detection With Hyperspectral Imaging for Food Safety Inspection. - Jungi Lee 001; Myounghwan Kim; Jiseong Yoon; Kwangsun Yoo; Seok-Joo Byun (IEEE Access, 2024)

Anomaly detection with hyperspectral imaging for food safety inspection. - Jungi Lee 001; Myounghwan Kim; Jiseong Yoon; Kwangsun Yoo; Seok-Joo Byun (IEEE DataPort, 2024)

Metric-Driven Similarity Indices: Redefining Spectral Distance Comparisons in Hyperspectral Data. - Jungkwon Kim; Jihoon Jung; Jungi Lee 001; Kwangsun Yoo; Seok-Joo Byun (ICAIIC, 2025)

Statistical Robustness in Anomaly Detection with Hard Rejection and Adaptive Weighting. - Jungi Lee 001; Jungkwon Kim; Chi Zhang; Kwangsun Yoo; Seok-Joo Byun (ICDM (Workshops), 2025)

Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss. - Jungi Lee 001; Jungkwon Kim; Chi Zhang; Sangmin Kim; Kwangsun Yoo; Seok-Joo Byun (IJCNN, 2025)

Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data. - Jungi Lee 001; Jungkwon Kim; Chi Zhang; Kwangsun Yoo; Seok-Joo Byun (CoRR, 2025)