We focus on the pool-based active learning, which is one of the most popular branches of active learning. The proposed quantum active learning algorithm can achieve quadratic speedup over the classical pool-based active learning.doi:10.1007/s11128-019-2460-xZhi-Min He...
Pool-Based 指无标记数据池直接被给出,主动学习算法在无标记数据池中选择部分数据进行标记。Batch指的是我们往往指定一个batch的数据进行标注,在利用这个batch的数据对模型更新后再次选择新的batch。Pool-Based Batch Active Learning模式也符合目前较为流行的深度学习范式。 考虑基于样本集合S得到的模型fS^与ground-truth...
1 Pool-based Active Learning in Approximate Linear Regression The goal of pool-based active learning is to choose the best input points to gather output values from a 'pool ' of input samples. We develop two pool-based active learning criteria for linear regression. The first criterion allows ...
libact: Pool-based Active Learning in Python authors: Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, Hsuan-Tien Lin Introduction libact is a Python package designed to make active learning easier for real-world users. The package not only implements several popular ac...
首先,作者们考虑了 pool-based batch active learning for noisy linear models。(即 f∗ 是一个线性模型,而且每一个数据点的 label 都是带噪声的),并提出了一套非常高效的算法,该算法仍然是基于 pseudo-label,即在不知道一个数据点的 label之前用当前模型给它安排的一个假label,在作者们的新算法中,每一个...
摘要: CiteSeerX - Scientific documents that cite the following paper: Employing EM in Pool-Based Active Learning for Text Classification会议名称: Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998 ...
A. Self-supervised deep-learning encodes high-resolution features of protein subcellular localization. Preprint at bioRxiv https://doi.org/10.1101/2021.03.29.437595 (2021). Strezoska, Ž. et al. High-content analysis screening for cell cycle regulators using arrayed synthetic crRNA libraries. J. ...
Two approaches are presented for this task: active classifier and weighted classifiers methods. Then the true labels are revealed and the pool is updated at the end of the batch. Updating the pool is done using one of the following methods: exact Bayesian, Bayesian and Heuristic. As the ...
Computer Science - LearningIn this paper we address the problem of pool based active learning, and provide an algorithm, called UPAL, that works by minimizing the unbiased estimator of the risk of a hypothesis in a given hypothesis space. For the space of linear classifiers and the squared ...
We focus on the pool-based active learning, which is one of the most popular branches of active learning. The proposed quantum active learning algorithm can achieve quadratic speedup over the classical pool-based active learning.doi:10.1007/s11128-019-2460-x...