4 papers
LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers
Minjun Kim, Jaeri Lee, Jongjin Kim +3
How can we accurately quantize a pre-trained Vision Transformer model? Quantization algorithms compress Vision Transformers (ViTs) into low-bit formats, reducing memory and computa…
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…
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…