Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings of these techniques involves the choice of their hyperparameters, since they have a significant impact on the final results. This work addresses the issue of fine-tuning hyperparameters of Deep Boltzmann Machines using metaheuristic optimization techniques with different backgrounds, such as swarm intelligence, memory- and evolutionary-based approaches. Experiments conducted in three public datasets for binary image reconstruction showed that metaheuristic techniques can obtain reasonable results.

翻译：过去几年来,深博尔茨曼机器等深层学习技术由于在一系列不同领域取得突出成果而受到相当的重视,这些技术的主要缺点之一是选择其超参数,因为它们对最终结果有重大影响。这项工作涉及利用具有不同背景的美术优化技术,如群集智能、记忆和进化方法,对深博尔茨曼机器的超参数进行微调的问题。在三个公共数据集中为二元图像重建进行的实验表明,美术技术可以取得合理的结果。