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Toward Length Extrapolatable Transformers

Offered By: Center for Language & Speech Processing(CLSP), JHU via YouTube

Tags

Transformers Courses Machine Learning Courses Neural Networks Courses Computational Linguistics Courses Attention Mechanisms Courses Language Models Courses Sequence Modeling Courses

Course Description

Overview

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Explore cutting-edge research on length extrapolation in Transformer models in this hour-long lecture by Ta-Chung Chi from Carnegie Mellon University. Delve into innovative approaches for improving the ability of Transformer architectures to handle sequences of varying lengths, a crucial challenge in natural language processing and machine learning. Gain insights into the latest techniques and methodologies aimed at enhancing the scalability and adaptability of these powerful models across different input sizes.

Syllabus

Toward Length Extrapolatable Transformers -- Ta-Chung Chi (CMU)


Taught by

Center for Language & Speech Processing(CLSP), JHU

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