File size: 7,691 Bytes
561f89a 90a1fbd 561f89a 0741783 561f89a 0741783 647d372 0741783 647d372 0741783 561f89a 0741783 561f89a 0741783 561f89a 0741783 561f89a 0741783 561f89a 0741783 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | ---
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! |