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This blog explores a semi-supervised approach to binary classification for topic modeling. The primary advantage of this approach is the reduced need for large labeled datasets, addressing a key limitation in supervised learning for natural language processing (NLP): achieving strong performance with minimal labeled data.
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Published in , 2024
In this paper we focus on the use of correlated quantizers, introduced by Suresh et al. in 2022, and demonstrate how these quantizers offer benefits over traditional independent quantizers in terms of communication complexity.
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Published in AISTATS, 2024
In this paper we introduce new SOTA algorithms in the realm of communication compression along with Byzantine robustness.
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Published in Neurips Math-AI, 2024
We study the ability of large language models to learn specific mathematical rules.
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Teaching assistant in mathematics, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.