Se Jung Kwon - RankMe

56 papers A* 14A 1Journal 34Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Affine-Scaled Attention: Towards Flexible and Stable Transformer Attention. CoRR Jeongin Bae, Baeseong Park, Gunho Park, Minsub Kim, Joonhyung Lee, Junhee Yoo, Sunghyeon Woo, Jiwon Ryu, Se Jung Kwon, Dongsoo Lee
2026	J	`jnl`	ICaRus: Identical Cache Reuse for Efficient Multi Model Inference. CoRR Sunghyeon Woo, Jaeeun Kil, Hoseung Kim, Minsub Kim, Joonghoon Kim, Ahreum Seo, Sungjae Lee, Minjung Jo, Jiwon Ryu, Baeseong Park, Se Jung Kwon, Dongsoo Lee
2026	J	`jnl`	PrefillShare: A Shared Prefill Module for KV Reuse in Multi-LLM Disaggregated Serving. CoRR Sunghyeon Woo, Hoseung Kim, Sunghwan Shim, Minjung Jo, Hyunjoon Jeong, Jeongtae Lee, Joonghoon Kim, Sungjae Lee, Baeseong Park, Se Jung Kwon, Dongsoo Lee
2026	J	`jnl`	SUN: Shared Use of Next-token Prediction for Efficient Multi-LLM Disaggregated Serving. CoRR Sunghyeon Woo, Ahreum Seo, Jaegwang Lee, Jaeeun Kil, Hanbae Seo, Joonghoon Kim, Baeseong Park, Se Jung Kwon, Dongsoo Lee
2026	J	`jnl`	Training-free Dropout Sampling for Semantic Token Acceptance in Speculative Decoding. CoRR Jeongtae Lee, Minjung Jo, Hyunjoon Jeong, Gunho Park, Sunghyeon Woo, Joonghoon Kim, Se Jung Kwon, Dongsoo Lee
2025	J	`jnl`	An Investigation of FP8 Across Accelerators for LLM Inference. CoRR JiWoo Kim, Joonhyung Lee, Gunho Park, Byeongwook Kim, Se Jung Kwon, Dongsoo Lee, Youngjoo Lee
2025	J	`jnl`	AnyBCQ: Hardware Efficient Flexible Binary-Coded Quantization for Multi-Precision LLMs. CoRR Gunho Park, Jeongin Bae, Beomseok Kwon, Byeongwook Kim, Se Jung Kwon, Dongsoo Lee
2025	J	`jnl`	CodeGEMM: A Codebook-Centric Approach to Efficient GEMM in Quantized LLMs. CoRR Gunho Park, Jeongin Bae, Byeongwook Kim, Baeseong Park, Jiwon Ryu, Hoseung Kim, Se Jung Kwon, Dongsoo Lee
2025	J	`jnl`	Debunking the CUDA Myth Towards GPU-based AI Systems. CoRR Yunjae Lee, Juntaek Lim, Jehyeon Bang, Eunyeong Cho, Huijong Jeong, Taesu Kim, Hyungjun Kim, Joonhyung Lee, Jinseop Im, Ranggi Hwang, Se Jung Kwon, Dongsoo Lee, Minsoo Rhu
2025	A*	`conf`	Debunking the CUDA Myth Towards GPU-based AI Systems: Evaluation of the Performance and Programmability of Intel's Gaudi NPU for AI Model Serving. ISCA Yunjae Lee, Juntaek Lim, Jehyeon Bang, Eunyeong Cho, Huijong Jeong, Taesu Kim, Hyungjun Kim, Joonhyung Lee, Jinseop Im, Ranggi Hwang, Se Jung Kwon, Dongsoo Lee, Minsoo Rhu
2025	J	`jnl`	Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models. CoRR Byeonghu Na, Minsang Park, Gyuwon Sim, DongHyeok Shin, HeeSun Bae, Mina Kang, Se Jung Kwon, Wanmo Kang, Il-Chul Moon
2025	J	`jnl`	Faster Inference of LLMs using FP8 on the Intel Gaudi. CoRR 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
2025	—	`conf`	LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices. NAACL (Long Papers) Jung Hyun Lee, Jeonghoon Kim, June Yong Yang, Se Jung Kwon, Eunho Yang, Kang Min Yoo, Dongsoo Lee
2025	J	`jnl`	SelfJudge: Faster Speculative Decoding via Self-Supervised Judge Verification. CoRR Kanghoon Yoon, Minsub Kim, Sungjae Lee, Joonhyung Lee, Sunghyeon Woo, Yeonjun In, Se Jung Kwon, Chanyoung Park, Dongsoo Lee
2025	—	`conf`	Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models. ACL (1) Seungcheol Park, Jeongin Bae, Beomseok Kwon, Minjun Kim, Byeongwook Kim, Se Jung Kwon, U Kang, Dongsoo Lee
2025	J	`jnl`	Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models. CoRR Seungcheol Park, Jeongin Bae, Beomseok Kwon, Minjun Kim, Byeongwook Kim, Se Jung Kwon, U Kang, Dongsoo Lee
2024	A*	`conf`	DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation. NeurIPS Sunghyeon Woo, Baeseong Park, Byeongwook Kim, Minjung Jo, Se Jung Kwon, Dongsuk Jeon, Dongsoo Lee
2024	J	`jnl`	LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices. CoRR Jung Hyun Lee, Jeonghoon Kim, June Yong Yang, Se Jung Kwon, Eunho Yang, Kang Min Yoo, Dongsoo Lee
2024	A*	`conf`	LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models. ICLR Gunho Park, Baeseong Park, Minsub Kim, Sungjae Lee, Jeonghoon Kim, Beomseok Kwon, Se Jung Kwon, Byeongwook Kim, Youngjoo Lee, Dongsoo Lee
2024	A*	`conf`	Label-Noise Robust Diffusion Models. ICLR Byeonghu Na, Yeongmin Kim, HeeSun Bae, Jung Hyun Lee, Se Jung Kwon, Wanmo Kang, Il-Chul Moon
2024	J	`jnl`	Label-Noise Robust Diffusion Models. CoRR Byeonghu Na, Yeongmin Kim, HeeSun Bae, Jung Hyun Lee, Se Jung Kwon, Wanmo Kang, Il-Chul Moon
2024	J	`jnl`	No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization. CoRR June Yong Yang, Byeongwook Kim, Jeongin Bae, Beomseok Kwon, Gunho Park, Eunho Yang, Se Jung Kwon, Dongsoo Lee
2024	A*	`conf`	Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models. ICLR Jung Hwan Heo, Jeonghoon Kim, Beomseok Kwon, Byeongwook Kim, Se Jung Kwon, Dongsoo Lee
2024	J	`jnl`	To FP8 and Back Again: Quantifying the Effects of Reducing Precision on LLM Training Stability. CoRR Joonhyung Lee, Jeongin Bae, Byeongwook Kim, Se Jung Kwon, Dongsoo Lee
2023	A*	`conf`	FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization. ICML Jung Hyun Lee, Jeonghoon Kim, Se Jung Kwon, Dongsoo Lee
2023	J	`jnl`	FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization. CoRR Jung Hyun Lee, Jeonghoon Kim, Se Jung Kwon, Dongsoo Lee
2023	A*	`conf`	Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization. NeurIPS Jeonghoon Kim, Jung Hyun Lee, Sungdong Kim, Joonsuk Park, Kang Min Yoo, Se Jung Kwon, Dongsoo Lee
2023	J	`jnl`	Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization. CoRR Jeonghoon Kim, Jung Hyun Lee, Sungdong Kim, Joonsuk Park, Kang Min Yoo, Se Jung Kwon, Dongsoo Lee
2023	A*	`conf`	Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models. ICML Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, Il-Chul Moon
2023	J	`jnl`	Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models. CoRR Jung Hwan Heo, Jeonghoon Kim, Beomseok Kwon, Byeongwook Kim, Se Jung Kwon, Dongsoo Lee
2023	—	`conf`	Sparsity-Aware Memory Interface Architecture using Stacked XORNet Compression for Accelerating Pruned-DNN Models. MLSys Younghoon Byun, Seungsik Moon, Baeseong Park, Se Jung Kwon, Dongsoo Lee, Gunho Park, Eunji Yoo, Jung Gyu Min, Youngjoo Lee
2023	A*	`conf`	TF-MVP: Novel Sparsity-Aware Transformer Accelerator with Mixed-Length Vector Pruning. DAC Eunji Yoo, Gunho Park, Jung Gyu Min, Se Jung Kwon, Baeseong Park, Dongsoo Lee, Youngjoo Lee
2023	A*	`conf`	Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic. ICLR Yulhwa Kim, Jaeyong Jang, Jehun Lee, Jihoon Park, Jeonghoon Kim, Byeongwook Kim, Baeseong Park, Se Jung Kwon, Dongsoo Lee, Jae-Joon Kim
2022	—	`conf`	AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models. EMNLP (Findings) Se Jung Kwon, Jeonghoon Kim, Jeongin Bae, Kang Min Yoo, Jin-Hwa Kim, Baeseong Park, Byeongwook Kim, Jung-Woo Ha, Nako Sung, Dongsoo Lee
2022	J	`jnl`	AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models. CoRR Se Jung Kwon, Jeonghoon Kim, Jeongin Bae, Kang Min Yoo, Jin-Hwa Kim, Baeseong Park, Byeongwook Kim, Jung-Woo Ha, Nako Sung, Dongsoo Lee
2022	A*	`conf`	Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression. ICLR Baeseong Park, Se Jung Kwon, Daehwan Oh, Byeongwook Kim, Dongsoo Lee
2022	A*	`conf`	Maximum Likelihood Training of Implicit Nonlinear Diffusion Model. NeurIPS Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-Chul Moon
2022	J	`jnl`	Maximum Likelihood Training of Implicit Nonlinear Diffusion Models. CoRR Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-Chul Moon
2022	J	`jnl`	nuQmm: Quantized MatMul for Efficient Inference of Large-Scale Generative Language Models. CoRR Gunho Park, Baeseong Park, Se Jung Kwon, Byeongwook Kim, Youngjoo Lee, Dongsoo Lee
2021	J	`jnl`	Modulating Regularization Frequency for Efficient Compression-Aware Model Training. CoRR Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Jeongin Yun, Baeseong Park, Yongkweon Jeon
2021	J	`jnl`	Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization. CoRR Byeongwook Kim, Dongsoo Lee, Yeonju Ro, Yongkweon Jeon, Se Jung Kwon, Baeseong Park, Daehwan Oh
2021	J	`jnl`	Sequential Encryption of Sparse Neural Networks Toward Optimum Representation of Irregular Sparsity. CoRR Baeseong Park, Se Jung Kwon, Dongsoo Lee, Daehwan Oh, Byeongwook Kim, Yongkweon Jeon, Yeonju Ro
2020	J	`jnl`	Adaptive Discrete Event Simulation Systems to Embrace Changes of Requirements Using Event Control Models. IEEE Trans. Syst. Man Cybern. Syst. Se Jung Kwon, Bong Gu Kang, Changbeom Choi, Tag Gon Kim
2020	J	`jnl`	BiQGEMM: Matrix Multiplication with Lookup Table For Binary-Coding-based Quantized DNNs. CoRR Yongkweon Jeon, Baeseong Park, Se Jung Kwon, Byeongwook Kim, Jeongin Yun, Dongsoo Lee
2020	A	`conf`	BiQGEMM: matrix multiplication with lookup table for binary-coding-based quantized DNNs. SC Yongkweon Jeon, Baeseong Park, Se Jung Kwon, Byeongwook Kim, Jeongin Yun, Dongsoo Lee
2020	—	`conf`	Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation. EMNLP (Findings) Insoo Chung, Byeongwook Kim, Yoonjung Choi, Se Jung Kwon, Yongkweon Jeon, Baeseong Park, Sangha Kim, Dongsoo Lee
2020	J	`jnl`	Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation. CoRR Insoo Chung, Byeongwook Kim, Yoonjung Choi, Se Jung Kwon, Yongkweon Jeon, Baeseong Park, Sangha Kim, Dongsoo Lee
2020	A*	`conf`	FleXOR: Trainable Fractional Quantization. NeurIPS Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Yongkweon Jeon, Baeseong Park, Jeongin Yun
2020	J	`jnl`	FleXOR: Trainable Fractional Quantization. CoRR Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Yongkweon Jeon, Baeseong Park, Jeongin Yun
2020	A*	`conf`	Structured Compression by Weight Encryption for Unstructured Pruning and Quantization. CVPR Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, Gu-Yeon Wei
2019	J	`jnl`	Learning Low-Rank Approximation for CNNs. CoRR Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Gu-Yeon Wei
2019	J	`jnl`	Network Pruning for Low-Rank Binary Indexing. CoRR Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Parichay Kapoor, Gu-Yeon Wei
2019	J	`jnl`	Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks. CoRR Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, Gu-Yeon Wei
2018	J	`jnl`	Simulation-Based Optimization on the System-of-Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network-Centric Warfare. Complex. Bong Gu Kang, Seon Han Choi, Se Jung Kwon, Jun Hee Lee, Tag Gon Kim
2013	—	`conf`	Integrated hybrid systems modeling and simulation methodology based on HDEVS formalism. SummerSim Se Jung Kwon, Changho Sung, Hae Sang Song, Tag Gon Kim
2012	—	`conf`	Design and implementation of event-based DEVS execution environment for faster execution of iterative simulation. SpringSim (TMS-DEVS) Se Jung Kwon, Tag Gon Kim