10 datasets found

Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences play a capital role in the search for accurate methods...

The dataset used in the paper is a large number of low-power agents (e.g., sensors) each measuring some quantity, such as whether there is a high or low concentration of a...

The Movielens-IMDB and Amazon datasets are used to evaluate the proposed algorithm.

A semi-supervised active domain adaptation framework for semantic segmentation in driving scenes.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a combination of datasets from the scikit-learn library and the UCI machine...

Select-by-Distinctive-Margin (SDM) is a simple yet effective active learning method for active domain adaptation.

Active learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each...

Generative Adversarial Active Learning (GAAL) algorithm using Generative Adversarial Networks (GAN) for active learning by query synthesis.

The proposed semi-automatic tract segmentation method is tested on a dataset containing tumor cases.

Modeling the behavior of coupled networks is challenging due to their intricate dynamics. For example in neuroscience, it is of critical importance to understand the...