Retraining-Based Iterative Weight Quantization for Deep Neural Networks. - Dongsoo Lee; Byeongwook Kim (CoRR, 2018)

DeepTwist: Learning Model Compression via Occasional Weight Distortion. - Dongsoo Lee; Parichay Kapoor; Byeongwook Kim (CoRR, 2018)

Network Pruning for Low-Rank Binary Indexing. - Dongsoo Lee; Se Jung Kwon; Byeongwook Kim; Parichay Kapoor; Gu-Yeon Wei (CoRR, 2019)

Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks. - Se Jung Kwon; Dongsoo Lee; Byeongwook Kim; Parichay Kapoor; Baeseong Park; Gu-Yeon Wei (CoRR, 2019)

Learning Low-Rank Approximation for CNNs. - Dongsoo Lee; Se Jung Kwon; Byeongwook Kim; Gu-Yeon Wei (CoRR, 2019)

Structured Compression by Weight Encryption for Unstructured Pruning and Quantization. - Se Jung Kwon; Dongsoo Lee; Byeongwook Kim; Parichay Kapoor; Baeseong Park; Gu-Yeon Wei (CVPR, 2020)

Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation. - Insoo Chung; Byeongwook Kim; Yoonjung Choi; Se Jung Kwon; Yongkweon Jeon; Baeseong Park; Sangha Kim 002; Dongsoo Lee (EMNLP (Findings), 2020)

FleXOR: Trainable Fractional Quantization. - Dongsoo Lee; Se Jung Kwon; Byeongwook Kim; Yongkweon Jeon; Baeseong Park; Jeongin Yun (NeurIPS, 2020)

BiQGEMM: matrix multiplication with lookup table for binary-coding-based quantized DNNs. - Yongkweon Jeon; Baeseong Park; Se Jung Kwon; Byeongwook Kim; Jeongin Yun; Dongsoo Lee (SC, 2020)

Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization. - Byeongwook Kim; Dongsoo Lee; Yeonju Ro; Yongkweon Jeon; Se Jung Kwon; Baeseong Park; Daehwan Oh (CoRR, 2021)

Sequential Encryption of Sparse Neural Networks Toward Optimum Representation of Irregular Sparsity. - Baeseong Park; Se Jung Kwon; Dongsoo Lee; Daehwan Oh; Byeongwook Kim; Yongkweon Jeon; Yeonju Ro (CoRR, 2021)

Modulating Regularization Frequency for Efficient Compression-Aware Model Training. - Dongsoo Lee; Se Jung Kwon; Byeongwook Kim; Jeongin Yun; Baeseong Park; Yongkweon Jeon (CoRR, 2021)

AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models. - Se Jung Kwon; Jeonghoon Kim; Jeongin Bae; Kang Min Yoo; Jin-Hwa Kim; Baeseong Park; Byeongwook Kim; Jung-Woo Ha 001; Nako Sung; Dongsoo Lee (EMNLP (Findings), 2022)

Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression. - Baeseong Park; Se Jung Kwon; Daehwan Oh; Byeongwook Kim; Dongsoo Lee (ICLR, 2022)

nuQmm: Quantized MatMul for Efficient Inference of Large-Scale Generative Language Models. - Gunho Park; Baeseong Park; Se Jung Kwon; Byeongwook Kim; Youngjoo Lee; Dongsoo Lee (CoRR, 2022)

Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic. - Yulhwa Kim; Jaeyong Jang; Jehun Lee; Jihoon Park; Jeonghoon Kim; Byeongwook Kim; Baeseong Park; Se Jung Kwon; Dongsoo Lee; Jae-Joon Kim (ICLR, 2023)

Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models. - Jung Hwan Heo; Jeonghoon Kim; Beomseok Kwon; Byeongwook Kim; Se Jung Kwon; Dongsoo Lee (ICLR, 2024)

LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models. - Gunho Park; Baeseong Park; Minsub Kim; Sungjae Lee 002; Jeonghoon Kim; Beomseok Kwon; Se Jung Kwon; Byeongwook Kim; Youngjoo Lee; Dongsoo Lee (ICLR, 2024)

DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation. - Sunghyeon Woo; Baeseong Park; Byeongwook Kim; Minjung Jo; Se Jung Kwon; Dongsuk Jeon; Dongsoo Lee (NeurIPS, 2024)

DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation. - Sunghyeon Woo; Baeseong Park; Byeongwook Kim; Minjung Jo; Sejung Kwon; Dongsuk Jeon; Dongsoo Lee (CoRR, 2024)

No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization. - June Yong Yang; Byeongwook Kim; Jeongin Bae; Beomseok Kwon; Gunho Park; Eunho Yang; Se Jung Kwon; Dongsoo Lee (CoRR, 2024)

To FP8 and Back Again: Quantifying the Effects of Reducing Precision on LLM Training Stability. - Joonhyung Lee; Jeongin Bae; Byeongwook Kim; Se Jung Kwon; Dongsoo Lee (CoRR, 2024)

Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models. - Seungcheol Park; Jeongin Bae; Beomseok Kwon; Minjun Kim 010; Byeongwook Kim; Se Jung Kwon; U Kang; Dongsoo Lee (ACL (1), 2025)

Secure Data Analytics in Apache Spark with Fine-grained Policy Enforcement and Isolated Execution. - Byeongwook Kim; Jaewon Hur; Adil Ahmad; Byoungyoung Lee (NDSS, 2025)

An Investigation of FP8 Across Accelerators for LLM Inference. - Jiwoo Kim; Joonhyung Lee; Gunho Park; Byeongwook Kim; Se Jung Kwon; Dongsoo Lee; Youngjoo Lee (CoRR, 2025)

Faster Inference of LLMs using FP8 on the Intel Gaudi. - Joonhyung Lee; Shmulik Markovich-Golan; Daniel Ohayon; Yair Hanani; Gunho Park; Byeongwook Kim; Asaf Karnieli; Uri Livne; Haihao Shen; Tai Huang; Se Jung Kwon; Dongsoo Lee (CoRR, 2025)