관심 주제를 입력하면 그 주제의 논문을 낸 연구실을 관련도 순으로 보여드려요. 순위가 아니라 근거 논문과 함께 보는 출발점이에요.
2026 논문이 아직 반영되지 않은 연구실은 결과에서 불리할 수 있어요.
Accurate Zero-shot Quantization via Hierarchical Teacher-Assistant Distillation
Wonjin Cho, Jeongin Yun, U Kang
SNU강유
LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers
Minjun Kim, Jaeri Lee, Jongjin Kim, Jeongin Yun, Yongmo Kwon, U Kang
SNU강유
Learning Diagnostic Clarifying Questions for Conversational Question Answering via Data-Efficient Dynamic Distillation
Jihyeong Jeon, Cheol Ryu, U Kang
SNU강유
Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
Minjun Kim, Jaehyeon Choi, Hyunwoo Yang, Jongjin Kim, Jinho Song, U Kang
SNU강유
ResCode: Accurate Expert Compression of Sparse MoE Models via Residual-code Restoration
Hyunwoo Yang, Minjun Kim, U Kang
SNU강유
SharVeT: Similarity-aware Parameter Sharing with Vector-based Tuning for Efficient LLM Compression
Jeongin Yun, Jaeri Lee, Jongjin Kim, Minjun Kim, Jinho Song, U Kang
SNU강유
LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents
Hyesung Jeon, Hyeongju Ha, Jae-Joon Kim
SNU김재준
QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models
Hyesung Jeon*, Seojune Lee*, Beomseok Kang, Yulhwa Kim, and Jae-Joon Kim
SNU김재준
LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss
E. Choi*, S. Song*, S. Yoo
SNU유승주
Locality-Aware Redundancy Pruning for LLM Depth Compression
V.-D. Yun*, Y. Kim*, W. Lim*, Y. Heo, M. Kim, S. Lee
SNU유승주
PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference
G. Song, S. Yoo
SNU유승주
HFSQVAE: Hierarchical Vector Quantization with Residuals for Frequency-Specific Embedding
Min Woo Kim, Seonji Park, Nam Ik Cho
SNU조남익
Q-Strata: Hierarchical Bit Allocation for Mixed-Precision Quantization of Mixture-of-Experts LLMs
Deokjae Lee, Sihun Chu, Hyun Oh Song
SNU송현오
Fractional Power-of-Two Quantization for Efficient and Effective Multiplier-Free LLM Inference
Sunghyun Wee, Geunjae Choi, Hyeonjin Kim, Suyoung Kim, Nojun Kwak
SNU곽노준
ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
Suyoung Kim, Sunghyun Wee, Hyeonjin Kim, Kyomin Hwang, Hyunho Lee, Nojun Kwak
SNU곽노준
Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models
Hyunho Lee*, Kyomin Hwang*, Hyeonjin Kim*, Suyoung Kim, Sunghyun Wee, Nojun Kwak
SNU곽노준
Distribution Matching Distillation without Fake Score Network
YoungJoong Kim, Deokyeong Lee, and Jaesik Park
SNU박재식
Stabilizing Consistency Training: A Flow Map Analysis and Self-Distillation
Youngjoong Kim, Duhoe Kim, Woosung Kim, and Jaesik Park
SNU박재식
TaskPress: Query-Agnostic KV Cache Compression via Task-Guided Pruning
Wonpyo Park, Seung-won Hwang.
SNU황승원
TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information
Dain Kwon, Kanghyun Choi, Hyeyoon Lee, Sunjong Park, Seoyong Lee, Sukjin Kim, Jinho Lee
SNU이진호
AdaRadar: Rate Adaptive Spectral Compression for Radar-based Perception
J H Park, S Y Chun, M G Seok
SNU전세영
DictLLM: Post-training Compression of Large Language Model with Dictionary Kernels
J H Park, S Y Chun, M G Seok
SNU전세영
An Adaptive Sampling Framework for Diffusion-based Dataset Distillation with High Fidelity and Diversity
Sunbeom Jeong, Sehwan Kim, Hyeonggeun Han, Hyungjun Joo, Sangwoo Hong, Jungwoo Lee
SNU이정우
Bias Alleviation through Network Pruning for Sparse and Debiased Models
Sangwoo Hong, Sehwan Kim, Hyungjun Joo, Hyeonggeun Han, Jiyoon Shin, Yoav Wald, and Jungwoo Lee
SNU이정우
HeSS: Head Sensitivity Score for Sparsity Redistribution in VGGT
Yongsung Kim, Wooseok Song, Jaihyun Lew, Hun Hwangbo, Jaehoon Lee, Sungroh Yoon
SNU윤성로
SSDi8: Accurate and Efficient 8-bit Quantization for State Space Duality
Hyunwoo Kim, Byoungchan Ko, Minseok Kang, Minwoo Kim, Dongjin Lee, Jaehoon Lee, Sungroh Yoon, Dahuin Jung
SNU윤성로
Garbage attention in large language models: Bos sink heads and sink-aware pruning
Jaewon Sok, Jewon Yeom, Seonghyeon Park, Jeongjae Park, Taesup Kim
SNU김태섭
FlexiQ: Adaptive Mixed-Precision Quantization for Latency/Accuracy Trade-Offs in Deep Neural Networks
Jaemin Kim, Hongjun Um, Sungkyun Kim, Yongjun Park, and Jiwon Seo
SNU서지원
Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning
Youngwoo Cho, Seunghoon Yi, Wooil Yang, Sungmo Kang, Young-Woo Son, Jaegul Choo, Joonseok Lee, Soo Kyung Kim, Hongkee Yoon
SNU이준석