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Respond to Limited or No Labeled Data in The Realm of NLP

7 minute read

Published:

Lack of labeled data for training is such a cliché in the data science and AI field. In data science, only large companies which accumulated sufficient user or machine records bother to research fancy predictive models to obtain a systematic understanding of the patterns with statistical evidence. Regarding AI, the algorithm development was once stuck at a bottleneck until the huge ImageNet dataset was open sourced which led to a blossom of neural network research in computer vision. In NLP, a decent, large go-to training set is still missing. To overcome this issue, researchers made enormous amount of effort in pre-training zero-shot or few-shot large language models from plain text.

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publications

Interpreting Polygenic Score Effects in Sibling Analysis

Published in bioRxiv, 2021

This paper is about pointing out the false assumptions in the Genome-wide sibling analysis method that has been widely used in the field of Genetics study.

Recommended citation: Jason Fletcher, Yuchang Wu, Tianchang Li, Qiongshi Lu. (2021). "Interpreting Polygenic Score Effects in Sibling Analysis." bioRxiv. doi: https://doi.org/10.1101/2021.07.16.452740. http://ltcrazy.github.io/files/PGS-in-sibling-analysis-bioRxiv.pdf

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.