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cs.LG2026
Fractional Power-of-Two Quantization for Efficient and Effective Multiplier-Free LLM Inference
Sunghyun Wee, Geunjae Choi, Hyeonjin Kim +2
Large Language Models (LLMs) demand efficient inference, where the multiply-accumulate (MAC) operations in linear projections dominate compute and energy cost. Post-training quanti…
cs.LG2026
Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models
Hyunho Lee, Kyomin Hwang, Hyeonjin Kim +3
With an increasing number of Object Detection (OD) models being deployed on edge devices, Zero-Shot Quantization for OD (ZSQ-OD) aims to quantize these models when access to the or…
cs.LG2025★ 3 cited
The Role of Teacher Calibration in Knowledge Distillation
Suyoung Kim, Seonguk Park, Junhoo Lee +1
Knowledge Distillation (KD) has emerged as an effective model compression technique in deep learning, enabling the transfer of knowledge from a large teacher model to a compact stu…