--- 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: 1. **Main Registry (This Repository):** Contains the complete searchable metadata, individual index files (`papers.parquet`), and partitioned per-venue Parquet files (`browse/`). 2. **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: ```bash pip install huggingface_hub ``` You can use the following script to look up a paper and pull its mirrored PDF automatically: ```python 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](https://huggingface.co/datasets/GenAI4ELab/papercli-papers) | | 2 | 📂 **NeurIPS** | [GenAI4ELab/papercli-papers-neurips](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-neurips) | | 3 | 📂 **AAAI** | [GenAI4ELab/papercli-papers-aaai](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-aaai) | | 4 | 📂 **EMNLP** | [GenAI4ELab/papercli-papers-emnlp](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-emnlp) | | 5 | 📂 **CVPR** | [GenAI4ELab/papercli-papers-cvpr](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-cvpr) | | 6 | 📂 **ICCV** | [GenAI4ELab/papercli-papers-iccv](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-iccv) | | 7 | 📂 **ICML** | [GenAI4ELab/papercli-papers-icml](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-icml) | | 8 | 📂 **ACL** | [GenAI4ELab/papercli-papers-acl](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-acl) | | 9 | 📂 **IJCAI** | [GenAI4ELab/papercli-papers-ijcai](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-ijcai) | | 10 | 📂 **ECCV** | [GenAI4ELab/papercli-papers-eccv](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-eccv) | | 11 | 📂 **ICLR** | [GenAI4ELab/papercli-papers-iclr](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-iclr) | | 12 | 📂 **NAACL** | [GenAI4ELab/papercli-papers-naacl](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-naacl) | | 13 | 📂 **Interspeech** | [GenAI4ELab/papercli-papers-interspeech](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-interspeech) | | 14 | 📂 **WACV** | [GenAI4ELab/papercli-papers-wacv](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-wacv) | | 15 | 📂 **JMLR** | [GenAI4ELab/papercli-papers-jmlr](https://huggingface.co/datasets/GenAI4ELab/papercli-papers-jmlr) | --- ## 🛠️ Credits & Tools This dataset was compiled and structured using **[papercli](https://github.com/Keithsel/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!