2 citations · 2 across the 6 of their papers we have counts for
6 papers
Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
Minjun Kim, Jaehyeon Choi, Hyunwoo Yang +3
What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve h…
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…
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…
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…
Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation
Jongjin Kim, Hyunsik Jeon, Jaeri Lee +1
When recommending personalized top- items to users, how can we recommend the items diversely to them while satisfying their needs? Aggregately diversified recommender systems ai…
Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge
Hyunsik Jeon, Jongjin Kim, Hoyoung Yoon +2
How can we accurately recommend actions for users to control their devices at home? Action recommendation for smart home has attracted increasing attention due to its potential imp…