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Hei Yi

As seen in: Color Research & Application, arXiv
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Articles

Computer Science > Machine Learning

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By Mehran Taghian, Anandharaju Durai, Hei Yi
| arXiv
Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent hardware support for low-precision formats, ranging from MXFP8 to ultra-low-bit formats such as MXFP4 and HiF4, has accelerated research into efficient MLLM training and deployment.
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Computer Science > Machine Learning

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By Hei Yi, Mehran Taghian, Anandharaju Durai
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (145) Issue with article?

Low-Resource NMT: A Case Study on the Written and Spoken Languages in Hong Kong

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By Hei Yi, Tan Jen Lee
| arXiv
Work on one of the world's most important websites and make an impact on open science.
Open in Who Shared View Duplicates (699) Issue with article?
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