Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find latent manifolds for classification and regression of complex experimental data. Like other ML problems, VAEs require hyperparameter tuning, e.g., balancing the Kullback Leibler (KL) and reconstruction terms. However, the training process and resulting manifold topology and connectivity depend not only on hyperparameters, but also their evolution during training. Because of the inefficiency of exhaustive search in a high-dimensional hyperparameter space for the expensive to train models, here we explored a latent Bayesian optimization (zBO) approach for the hyperparameter trajectory optimization for the unsupervised and semi-supervised ML and demonstrate for joint-VAE with rotational invariances. We demonstrate an application of this method for finding joint discrete and continuous rotationally invariant representations for MNIST and experimental data of a plasmonic nanoparticles material system. The performance of the proposed approach has been discussed extensively, where it allows for any high dimensional hyperparameter tuning or trajectory optimization of other ML models.
翻译:由于物理、化学和材料科学等多个领域的物理、化学和材料科学领域都有能力脱钩,而且能够找到用于复杂实验数据分类和回归的隐性柱体,因此这些不受监督和半监督的ML方法被广泛采用。与其他ML问题一样,VAE系统需要超参数调整,例如平衡Kullback Leibel (KL) 和重建条件。然而,培训过程和由此产生的多重地形学和连通性不仅取决于超光谱度计,而且还取决于其培训过程中的演变。由于高分辨率超光谱空间用于培训模型的详尽搜索效率低下,我们在这里探讨了一种潜在的Bayesian优化(zBO)方法,用于为无监督和半监督的ML优化超光谱轨迹轨道,并展示了联合VAE(KL)与轮替变化条件之间的平衡。我们展示了这一方法的应用,用以寻找MNIST和实验性高分辨率超光谱纳米粒子模型的连续旋转图示和实验数据。我们广泛讨论了该模型的运行情况。