6 papers
SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning
Minjun Kim, Jongjin Kim, U Kang
How can we accurately quantize a pre-trained model without any data? Quantization algorithms are widely used for deploying neural networks on resource-constrained edge devices. Zer…
Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models
Seungcheol Park, Jeongin Bae, Beomseok Kwon +5
How can we quantize large language models while preserving accuracy? Quantization is essential for deploying large language models (LLMs) efficiently. Binary-coding quantization (B…
Accurate Sublayer Pruning for Large Language Models by Exploiting Latency and Tunability Information
Seungcheol Park, Sojin Lee, Jongjin Kim +3
How can we accelerate large language models(LLMs) without sacrificing accuracy? The slow inference speed of LLMs hinders us to benefit from their remarkable performance in diverse…
Zero-shot Quantization: A Comprehensive Survey
Minjun Kim, Jaehyeon Choi, Jongkeun Lee +2
Network quantization has proven to be a powerful approach to reduce the memory and computational demands of deep learning models for deployment on resource-constrained devices. How…
AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
Minjun Kim, Jaehyeon Choi, SeungJoo Lee +2
How can we accurately classify graphs? Graph classification is a pivotal task in data mining with applications in social network analysis, web analysis, drug discovery, molecular p…
Susceptibility of Large Language Models to User-Driven Factors in Medical Queries
Kyung Ho Lim, Ujin Kang, Xiang Li +4
Large language models (LLMs) are increasingly used in healthcare, but their reliability is heavily influenced by user-driven factors such as question phrasing and the completeness…