Datasets for Machine-Learning Research

Dec 21, 2023 ·

The List of Datasets for Machine-Learning Research is a comprehensive compilation of datasets used in machine-learning research. It contains a wide range of data from a variety of domains, including natural language processing, computer vision, robotics, game theory, and more. Most of the datasets are publicly available for research use, and some can be obtained through commercial sources. The list includes both open source and proprietary datasets and includes details about the types of data contained in each dataset. It also provides information on how to get access to the datasets and where to find them online.

In terms of natural language processing (NLP), the list contains datasets related to text classification, sentiment analysis, question answering, summarization, dialogue systems, translation, language modeling, and other NLP tasks. For example, the Stanford Question Answering Dataset (SQuAD) consists of over 100K questions written by crowdworkers that require a short paragraph of text as an answer. In terms of computer vision, the list contains datasets related to object detection, image segmentation, and image classification. Examples include the ImageNet dataset, which contains millions of images labeled with various objects and scenes, and the COCO dataset, which contains a large number of annotated images.

Robotics datasets are also included in the list, such as the DARPA Robotics Challenge dataset, which is composed of real robotic arms performing various tasks. This dataset was created by the US Defense Advanced Research Projects Agency (DARPA) to develop autonomous robots capable of responding to disasters. Game theory datasets are also available, like the Go dataset, which consists of over 500,000 moves taken from games of Go played between two professional players. Other datasets cover a variety of topics, such as the IMDb movie review dataset, US Census data, and the Million Song Dataset.

Overall, the List of Datasets for Machine-Learning Research is an invaluable resource for machine-learning researchers. By providing access to a wide variety of datasets from various domains, it allows them to experiment and find new insights into their respective fields.