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Yong-Ju Lee

2 accepted papers

2024

KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis

NeurIPS 2024poster

As text-to-image (T2I) synthesis models increase in size, they demand higher inference costs due to the need for more expensive GPUs with larger memory, which makes it challenging to reproduce these models in addition to the restricted access to training datasets. Our study aims to reduce these infe…

Cited by 2SourcePDFScholar
2021

Block-wise Word Embedding Compression Revisited: Better Weighting and Structuring

EMNLP 2021finding

Word embedding is essential for neural network models for various natural language processing tasks. Since the word embedding usually has a considerable size, in order to deploy a neural network model having it on edge devices, it should be effectively compressed. There was a study for proposing a b…