论文标题
揭示看不见的：隐藏在噪音中的信息
Unveil the unseen: Exploit information hidden in noise
论文作者
论文摘要
噪音和不确定性通常是机器学习的敌人，训练数据中的噪声会导致预测中的不确定性和不准确性。但是，我们开发了一种机器学习体系结构，该体系结构从噪声本身中提取重要信息以改善预测。现象学计算，然后在一个目标变量中利用不确定性来预测第二个目标变量。我们将这种形式主义应用于pbzr $ _ {0.7} $ sn $ _ {0.3} $ o $ $ _ {3} $ crystal，使用介电常数的不确定性来推断热容量，并正确预测了相位过渡，否则无法算力。在第二个示例中 - 液滴的单粒子衍射 - 我们利用粒子计数及其不确定性来推断地面真相衍射幅度，提供比仅利用粒子计数时更好的预测。我们的通用形式主义可以在机器学习中剥削不确定性，这在物理科学及其他地区具有广泛的应用。
Noise and uncertainty are usually the enemy of machine learning, noise in training data leads to uncertainty and inaccuracy in the predictions. However, we develop a machine learning architecture that extracts crucial information out of the noise itself to improve the predictions. The phenomenology computes and then utilizes uncertainty in one target variable to predict a second target variable. We apply this formalism to PbZr$_{0.7}$Sn$_{0.3}$O$_{3}$ crystal, using the uncertainty in dielectric constant to extrapolate heat capacity, correctly predicting a phase transition that otherwise cannot be extrapolated. For the second example -- single-particle diffraction of droplets -- we utilize the particle count together with its uncertainty to extrapolate the ground truth diffraction amplitude, delivering better predictions than when we utilize only the particle count. Our generic formalism enables the exploitation of uncertainty in machine learning, which has a broad range of applications in the physical sciences and beyond.
