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Hwee Kuan Lee on Muck Rack

Hwee Kuan Lee

Singapore
As seen in: arXiv
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Articles

[2406.02847] Exact Conversion of In-Context Learning to Model Weights

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By Hwee Kuan, Tianyang Hu, Hui Jin, Hwee Kuan Lee
| 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.
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Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis

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By Davide Coppola, Hwee Kuan Lee, Malini Olivo, Mahsa Paknezhad
| osapublishing.org
S.-Q. Wang, X.-Y. Zhang, J. Liu, C. Tao, C.-Y. Zhu, C. Shu, T. Xu, and H.-Z. Jin, “Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population,” Chin Med J (Engl) 133(17), 2027–2036 (2020). [Crossref] S. Jinnai, N. Yamazaki, Y. Hirano, Y. Sugawara, Y. Ohe, and R.
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Resolution enhancement and realistic speckle recovery with generative adversarial modeling of micro-optical coherence tomography

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By Jun Xie, Linbo Liu, Hwee Kuan Lee, Kaicheng Liang
| osapublishing.org
G. Barbastathis, A. Ozcan, and G. Situ, “On the use of deep learning for computational imaging,” Optica 6(8), 921–943 (2019). [Crossref] C. Belthangady and L. A. Royer, “Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction,” Nat. Methods 16(12), 1215–1225 (2019). [Crossref] H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydin, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat.
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