README.md

---
language:
- en
size_categories:
- 10K<n<100K
---

Thank @sunbread for his spider code to get posters.


# Research Materials Overview

This document provides an organized overview of the contents extracted from the `data.zip` file, detailing the structure and content of the extracted folders, each named with a unique hash code. The folders contain valuable academic resources, including LaTeX source code for research papers and associated posters, as well as metadata in `match_arxiv.json` files that include titles, abstracts, and other relevant information.

## Folder Structure

After extracting `data.zip`, you will find a series of folders, each uniquely identified by a hash code. These folders are structured as follows:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6541d5f1c7bf7cd712511fc6/lp6tXsEL0PEhMwQmw8rAQ.png)

## Contents Description

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6541d5f1c7bf7cd712511fc6/8yNQfF6rVGeUj9p-ucefl.png)

### LaTeX Source Code

- **Filename:** `id.tar.gz`
- **Description:** Contains the LaTeX source code of the research paper.

### Poster

- **Filename:** `poster.pdf or .png or .others`
- **Description:** The visual summary of the research, designed for presentation purposes. Posters typically include the study's main findings, illustrations, and summaries of the research methods and conclusions.

### Metadata JSON

- **Filename:** `match_arxiv.json`
- **Description:** This JSON file contains metadata related to the research paper, including:
  - **Title:** The title of the paper.
  - **Abstract:** A brief summary of the research, its objectives, main findings, and conclusions.
  - Other relevant information which may include authors, publication date, keywords, etc.


|    | type   | name                                                                    | virtualsite_url                            | speakers/authors                                                                                         | abstract_x                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | poster_url                                                                   | poster_hash                                                      |      id | submitter            | authors                                                                | title                                                                   | comments                                                        | journal-ref   | doi   | report-no   | categories          | license                                             | abstract_y                                                                      | versions                                                                                                                                                                                                                                                  | update_date   | authors_parsed                                                                                                                                                                          |
|---:|:-------|:------------------------------------------------------------------------|:-------------------------------------------|:---------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:-----------------------------------------------------------------|--------:|:---------------------|:-----------------------------------------------------------------------|:------------------------------------------------------------------------|:----------------------------------------------------------------|:--------------|:------|:------------|:--------------------|:----------------------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|  0 | Poster | rm a2q aggregationaware quantization for graph neural networks          | https://iclr.cc//virtual/2023/poster/11655 | Zeyu Zhu, Fanrong Li, Zitao Mo, Qinghao Hu, Gang Li, Zejian Liu, Xiaoyao Liang, Jian Cheng               | abs| https://iclr.cc//media/PosterPDFs/ICLR%202023/11655.png?t=1680841903.3493876 | fa1b213e12a3d15c6a1aa1e9bee62be7510b7ad5d6eb229aa20578cc718c7675 | 2302    | Zeyu Zhu             | Zeyu Zhu, Fanrong Li, Zitao Mo, Qinghao Hu, Gang Li, Zejian Liu,       | rm a2q aggregationaware quantization for graph neural networks          | Accepted by ICLR2023                                            |               |       |             | cs.LG               | http://creativecommons.org/licenses/by/4.0/         | As graph data size increases, the vast latency and memory consumption during    | [{'version': 'v1', 'created': 'Wed, 1 Feb 2023 02:54:35 GMT'}]                                                                                                                                                                                            | 2023-02-02    | [['Zhu', 'Zeyu', ''], ['Li', 'Fanrong', ''], ['Mo', 'Zitao', ''], ['Hu', 'Qinghao', ''], ['Li', 'Gang', ''], ['Liu', 'Zejian', ''], ['Liang', 'Xiaoyao', ''], ['Cheng', 'Jian', '']]    |



## Accessing the Information

To access a specific paper's LaTeX source code, poster, or metadata, navigate to the corresponding folder named with the hash code associated with that paper. Each folder contains all the resources related to a single research paper, allowing for easy access and review.