metadata
license: cc-by-4.0
pretty_name: AI Conference & Journal Papers
configs:
- config_name: aaai
data_files:
- split: '2026'
path: browse/aaai/2026.parquet
- split: '2025'
path: browse/aaai/2025.parquet
- split: '2024'
path: browse/aaai/2024.parquet
- split: '2023'
path: browse/aaai/2023.parquet
- config_name: acl
data_files:
- split: '2025'
path: browse/acl/2025.parquet
- split: '2024'
path: browse/acl/2024.parquet
- split: '2023'
path: browse/acl/2023.parquet
- config_name: cvpr
data_files:
- split: '2026'
path: browse/cvpr/2026.parquet
- split: '2025'
path: browse/cvpr/2025.parquet
- split: '2024'
path: browse/cvpr/2024.parquet
- split: '2023'
path: browse/cvpr/2023.parquet
- config_name: eccv
data_files:
- split: '2024'
path: browse/eccv/2024.parquet
- split: '2022'
path: browse/eccv/2022.parquet
- split: '2020'
path: browse/eccv/2020.parquet
- config_name: emnlp
data_files:
- split: '2025'
path: browse/emnlp/2025.parquet
- split: '2024'
path: browse/emnlp/2024.parquet
- split: '2023'
path: browse/emnlp/2023.parquet
- config_name: iccv
data_files:
- split: '2025'
path: browse/iccv/2025.parquet
- split: '2023'
path: browse/iccv/2023.parquet
- config_name: iclr
data_files:
- split: '2026'
path: browse/iclr/2026.parquet
- split: '2025'
path: browse/iclr/2025.parquet
- split: '2024'
path: browse/iclr/2024.parquet
- split: '2023'
path: browse/iclr/2023.parquet
- config_name: icml
data_files:
- split: '2025'
path: browse/icml/2025.parquet
- split: '2024'
path: browse/icml/2024.parquet
- split: '2023'
path: browse/icml/2023.parquet
- config_name: ijcai
data_files:
- split: '2025'
path: browse/ijcai/2025.parquet
- split: '2024'
path: browse/ijcai/2024.parquet
- split: '2023'
path: browse/ijcai/2023.parquet
- config_name: interspeech
data_files:
- split: '2025'
path: browse/interspeech/2025.parquet
- split: '2024'
path: browse/interspeech/2024.parquet
- split: '2023'
path: browse/interspeech/2023.parquet
- config_name: jmlr
data_files:
- split: '2025'
path: browse/jmlr/2025.parquet
- split: '2024'
path: browse/jmlr/2024.parquet
- split: '2023'
path: browse/jmlr/2023.parquet
- split: '2022'
path: browse/jmlr/2022.parquet
- config_name: naacl
data_files:
- split: '2025'
path: browse/naacl/2025.parquet
- split: '2024'
path: browse/naacl/2024.parquet
- config_name: neurips
data_files:
- split: '2025'
path: browse/neurips/2025.parquet
- split: '2024'
path: browse/neurips/2024.parquet
- split: '2023'
path: browse/neurips/2023.parquet
- config_name: wacv
data_files:
- split: '2026'
path: browse/wacv/2026.parquet
- split: '2025'
path: browse/wacv/2025.parquet
- split: '2024'
path: browse/wacv/2024.parquet
- split: '2023'
path: browse/wacv/2023.parquet
AI Conference & Journal Papers
Searchable metadata and full-text PDF mirrors for papers from top-tier AI venues (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, WACV, ACL, EMNLP, NAACL, IJCAI, AAAI, JMLR, Interspeech) from 2023.
- 📊
papers.parquet: The complete dataset containing all fields and all venues. - 🔍 Per-venue browse views: Easily explore specific subsets by selecting a venue in Subset and a year in Split.
🏗️ Dataset Structure & Storage Strategy
To avoid reaching repository size limits and ensure optimal performance, the project is decoupled into two components:
- Main Registry (This Repository): Contains the complete searchable metadata, individual index files (
papers.parquet), and partitioned per-venue Parquet files (browse/). - PDF Storage Shards: The raw PDF binary files are sharded into separate, venue-specific repositories (
GenAI4ELab/papercli-papers-[venue]).
🛠️ How to Download PDFs
Because the metadata and actual file pointers reside in this main repository, the standard workflow is to query/filter the metadata here first, then programmatically fetch the corresponding PDF binary from its respective shard.
Python Example
Ensure you have the Hugging Face Hub CLI client installed:
pip install huggingface_hub
You can use the following script to look up a paper and pull its mirrored PDF automatically:
from huggingface_hub import hf_hub_download
# Assuming `row` is a dictionary or pandas row obtained from the metadata Parquet
venue_name = row['venue'].lower()
repo_id = f"GenAI4ELab/papercli-papers-{venue_name}"
path = hf_hub_download(
repo_id=repo_id,
filename=row["hf_pdf_path"],
repo_type="dataset",
)
print(f"Downloaded PDF to: {path}")
🔗 Dataset Hub & Venue Directory
Here is the complete navigation map for the main dataset metadata registry and all corresponding sharded PDF storage repositories:
| STT | Venue / Dataset | Repository Link |
|---|---|---|
| 1 | 🏠 Main Registry (Metadata) | GenAI4ELab/papercli-papers |
| 2 | 📂 NeurIPS | GenAI4ELab/papercli-papers-neurips |
| 3 | 📂 AAAI | GenAI4ELab/papercli-papers-aaai |
| 4 | 📂 EMNLP | GenAI4ELab/papercli-papers-emnlp |
| 5 | 📂 CVPR | GenAI4ELab/papercli-papers-cvpr |
| 6 | 📂 ICCV | GenAI4ELab/papercli-papers-iccv |
| 7 | 📂 ICML | GenAI4ELab/papercli-papers-icml |
| 8 | 📂 ACL | GenAI4ELab/papercli-papers-acl |
| 9 | 📂 IJCAI | GenAI4ELab/papercli-papers-ijcai |
| 10 | 📂 ECCV | GenAI4ELab/papercli-papers-eccv |
| 11 | 📂 ICLR | GenAI4ELab/papercli-papers-iclr |
| 12 | 📂 NAACL | GenAI4ELab/papercli-papers-naacl |
| 13 | 📂 Interspeech | GenAI4ELab/papercli-papers-interspeech |
| 14 | 📂 WACV | GenAI4ELab/papercli-papers-wacv |
| 15 | 📂 JMLR | GenAI4ELab/papercli-papers-jmlr |
🛠️ Credits & Tools
This dataset was compiled and structured using papercli, an open-source tool designed to index, mirror, and shard academic papers from top-tier AI venues efficiently.
If you find this mirror useful, please consider starring the parent repository and the original papercli project!