ML Paper Summaries

Summaries and critiques of papers (mostly in machine learning) I’ve read. This is not a summary of the traditional sense which will carefully go over all the major concepts in the paper (due to time constraints); instead, it will be rather concise and only contain the key points that I find interesting, with the expectation that the reader already has some familiarity with the paper.

This serves to both catalog my own reading and academic progress, and may also be of interest to others to find interesting papers to check out.

The format is inspired by the paper summaries.

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  1. (2023) Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
  2. (2023) Deep contextualized word representations (ELMo)