Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formulates batch active learning from the sparse approximation's perspective. Our active learning method aims to find an informative subset from the unlabeled data pool such that the corresponding training loss function approximates its full data pool counterpart. We realize the framework as sparsity-constrained discontinuous optimization problems, which explicitly balance uncertainty and representation for large-scale applications and could be solved by greedy or proximal iterative hard thresholding algorithms. The proposed method can adapt to various settings, including both Bayesian and non-Bayesian neural networks. Numerical experiments show that our work achieves competitive performance across different settings with lower computational complexity.
翻译:积极学习通过利用机器学习代理人和人类笔记员之间的相互作用,使高效模式培训成为有效的模式培训。 我们研究并提议一个新的框架,从少许近似的角度来分批积极学习。 我们的积极学习方法旨在从未贴标签的数据库中找到一个信息子集,使相应的培训损失函数接近其完整的数据库对应方。 我们认识到这个框架是零散的、受限制的不连续优化问题,它明确平衡了大规模应用的不确定性和代表性,并且可以通过贪婪或近似迭代硬阈值算法加以解决。 提议的方法可以适应各种环境, 包括巴耶斯和非拜伊斯神经网络。 数字实验表明,我们的工作在不同环境中取得了竞争性业绩,而计算复杂性较低。