Research Papers
Explore an ever-evolving repository of AI wisdom, showcasing the latest, most influential, and historically significant papers, thoughtfully organized by their publication dates. Our weekly updates guarantee access to the forefront of AI research.
How to Read Research Papers
- How to Read Research Papers: A Pragmatic Approach for ML Practitioners (opens in a new tab): This post offers steps for effectively reading machine learning research papers: topic selection, skimming title, abstract, conclusion, delving into the introduction, examining figures, deep reading, and clarifying unfamiliar terms. A valuable resource for newcomers.
Where to Read Research Papers
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Pretrain, Prompt, Predict (opens in a new tab): The website offers a prompt learning timeline, paper list, and GitHub repository. It's a valuable resource for those interested in exploring research papers on pre-training and prompt-based learning in NLP.
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Papers with Code (opens in a new tab): The website compiles research papers in machine learning and AI, offering accompanying code. It features a search function, with each paper providing a summary, paper link, and code link for implementation.
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arXiv (opens in a new tab): An open-source, open-access repository that plays a crucial role in the machine learning community. It hosts electronic preprints and postprints, providing a wealth of papers and resources for staying informed and sharing knowledge in the field.
Reference
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arXiv-sanity lite (opens in a new tab): Users can tag papers of interest, receiving recommendations for each tag using SVMs over tf-idf features from paper abstracts. It offers search, ranking, sorting, and customization in a user-friendly web interface. Lastly, arxiv-sanity-lite provides daily email recommendations based on your tags. (code)
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Paper with Code State-of-the-Art (opens in a new tab): Explore research papers with accompanying code for enhanced understanding and practical application.
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Mathematical Notation: A Guide for Engineers and Scientists (opens in a new tab) is a concise reference book designed to help engineers & scientists understand common mathematical symbols. Organized by topic, it provides explanations for each symbol, along with guidance on producing them in LaTeX. The book serves as a valuable resource for quickly grasping mathematical notation used in research papers, offering clarity without delving into the underlying mathematics.