Papers
A curated list of the top 100 most cited deep learning papers from 2012-2016, serving as a foundational reading list.
What is Papers?
Awesome Deep Learning Papers is a curated GitHub repository listing the top 100 most cited academic papers in deep learning from 2012 to 2016. It serves as a foundational reading list to help researchers and engineers understand the seminal works that shaped the modern deep learning landscape. The project filters the vast literature to highlight papers with the highest impact and broadest applicability.
Deep learning researchers, graduate students, and practitioners who want a structured, high-quality entry point into the core literature of the field without being overwhelmed by its volume.
It provides a community-vetted, citation-based filter to identify the most influential papers, saving time and offering a clear learning path. Unlike broader paper lists, it focuses on a critical historical period and maintains a limited, manageable size to ensure quality over quantity.
Overview
The most cited deep learning papers
Use Cases
Best For
- Graduate students starting deep learning research and needing a reading roadmap
Related Projects
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