This repository serves as an archive for a Deep Learning Paper Reading Meeting, cataloging key research papers and their summaries. It's designed for researchers, students, and practitioners in deep learning and NLP who want to quickly grasp the essence and impact of seminal papers. The archive provides a structured overview of papers, their links, and key contributions, facilitating learning and discussion.

How It Works

The archive is organized as a table, listing papers by category (primarily NLP, with some Vision and Fundamental ML topics). Each entry includes the paper's title, a link to its presentation (often YouTube), a brief description of its core contribution or technique, and highlights of its impact. This structured format allows users to efficiently browse and identify papers relevant to specific areas of deep learning research.

Quick Start & Requirements

• Access the archive directly via the GitHub repository's README.
• No specific software installation is required to view the archive.
• Links provided may require internet access and compatible media players (e.g., YouTube).

Highlighted Details

• Comprehensive coverage of foundational NLP papers like "Attention Is All You Need" (Transformer) and BERT.
• Includes papers on various NLP tasks such as machine translation, text summarization, and question answering.
• Features a growing collection of Vision papers, covering object detection, segmentation, and image generation.
• Fundamental ML concepts like optimization algorithms (AdamW, RAdam) and regularization (Dropout) are also cataloged.

Maintenance & Community

• The project appears to be maintained by the contributor(s) of the GitHub repository.
• Community interaction is encouraged via GitHub issues or email (tfkeras@kakao.com).

Licensing & Compatibility

• The repository itself is likely under a permissive open-source license (e.g., MIT, Apache), common for such archives.
• The linked papers and their content are subject to their original publication licenses and copyright.

Limitations & Caveats

• This is an archive and does not provide code implementations for the papers listed.
• The "Link" column primarily points to presentation videos, not necessarily original papers or code repositories.
• Coverage is extensive but may not be exhaustive of all significant deep learning research.