论文标题
全息图 - （V）AE：端到端SO（3） - 等级（变异）自动编码器在傅立叶空间中
Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space
论文作者
论文摘要
群体等级神经网络已成为一种求解分类和回归任务的数据效率方法，同时尊重数据的相对对称性。但是，几乎没有做过将此范式扩展到无监督和生成域的工作。在这里，我们介绍了全息（变异）自动编码器（H-（V）AE），这是傅立叶空间中完全端到端的SO（3） - 等级（变异）自动编码器，适用于3D中指定的原点周围分布在指定的原点周围的数据。 H-（V）AE经过训练，可以重建数据的球形傅立叶编码，在过程中学习数据的低维表示（即潜在空间），并具有最大信息的旋转旋转嵌入，并与刻度的框架一起描述了描述数据方向。我们广泛测试了在不同数据集上H-（V）AE的性能。我们表明，学习的潜在空间有效地编码球形图像的分类特征。此外，H-（v）AE的潜在空间可用于提取蛋白质结构微环境的紧凑型嵌入，并且与随机森林回归剂配对时，它可以实现对蛋白质配体结合亲和力的最新预测。
Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsupervised and generative domains. Here, we present Holographic-(Variational) Auto Encoder (H-(V)AE), a fully end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space, suitable for unsupervised learning and generation of data distributed around a specified origin in 3D. H-(V)AE is trained to reconstruct the spherical Fourier encoding of data, learning in the process a low-dimensional representation of the data (i.e., a latent space) with a maximally informative rotationally invariant embedding alongside an equivariant frame describing the orientation of the data. We extensively test the performance of H-(V)AE on diverse datasets. We show that the learned latent space efficiently encodes the categorical features of spherical images. Moreover, H-(V)AE's latent space can be used to extract compact embeddings for protein structure microenvironments, and when paired with a Random Forest Regressor, it enables state-of-the-art predictions of protein-ligand binding affinity.
