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Papers with Code links research papers in AI with open-source code, promoting accessibility and innovation.
Provides easy access to research papers and their corresponding code.
Quality of code varies across projects.
ArXiv hosts a vast collection of research papers across multiple disciplines, including AI and machine learning.
Extensive repository of research papers across various fields.
No direct links to code implementations.
GitHub is a platform for hosting and sharing open-source projects, including many implementations of machine learning algorithms.
Widely used for sharing code with version control and collaboration tools.
Lacks dedicated research paper integration.
Kaggle offers datasets, notebooks, and competitions, helping practitioners apply machine learning techniques.
Great resource for datasets and applied learning opportunities.
Focuses more on competitions than linking research papers.
Google Scholar provides access to scholarly articles and citations across various disciplines.
Excellent for finding academic papers and tracking citations.
No integration with code or implementations.
Semantic Scholar uses AI to provide enhanced search and insights for academic papers.
AI-driven insights and recommendations for research papers.
Limited emphasis on open-source code integration.