Awesome-Quantization-Papers

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Awesome-Quantization-Papers is a curated list of research papers focused on neural network quantization. It helps researchers and practitioners find relevant studies categorized by model structures and application scenarios.

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About

What is Awesome-Quantization-Papers?

Awesome-Quantization-Papers provides a comprehensive, curated collection of research papers specifically on neural network quantization. The repository includes papers from recent AI conferences and journals, offering a valuable resource for those interested in this specialized field. It systematically categorizes papers based on their underlying model structures and the application scenarios they address. Furthermore, it enhances discoverability by labeling various quantization methods with relevant keywords, streamlining the process of finding specific research.

Best used for

Finding and organizing research papers related to neural network quantization for academic or professional purposes.

Common actions

Research neural network quantization

Find relevant academic papers

Explore quantization methods

Stay updated on AI research

Understand model optimization

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Capabilities

Key features

Curated paper list
Neural network quantization
Categorized by model
Keyword-labeled methods
Covers recent conferences

Target Audience

AI ResearchersMachine Learning EngineersAcademicsStudents

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Is there a way to contribute new papers or suggest improvements to the Awesome-Quantization-Papers list?

Yes, as an open-source curated list, contributions are typically welcome. Users can usually submit pull requests on the repository's platform (e.g., GitHub) to add new papers, correct information, or suggest categorization improvements, following the project's contribution guidelines.

How frequently is the Awesome-Quantization-Papers list updated with new research?

The update frequency depends on the maintainers' activity and the pace of new research in the field. Curated lists like this are often updated periodically, especially after major AI conferences, but there's usually no guaranteed schedule. Users can check the repository's commit history for recent activity.

Does the repository include code implementations or just the research papers?

Awesome-Quantization-Papers primarily focuses on listing and categorizing research papers. While some paper entries might link to official code repositories if available, the main purpose is to provide a curated bibliography rather than a collection of code implementations.

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