Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks
基于无监督卷积长短期记忆网络的时空表示学习
机构 * Department of Physics and Astronomy, University of Minnesota Twin Cities（物理与天文学系，明尼苏达大学双城分校） ； Physics Department, Lancaster University（拉斯特纳大学物理系） ； European Space Agency (ESA), European Space Astronomy Centre (ESAC)（欧洲航天局（ESA）、欧洲空间天文学中心（ESAC）） ； Departments of Physics and Astronomy, Haverford College（物理与天文学系，哈弗德学院） ； School of Physical Sciences, The Open University（物理科学学院，开放大学）
AI总结 本文提出了一种基于无监督卷积长短期记忆网络的深度学习框架，用于在空间和光谱维度上学习星系的通用特征表示，并在活跃星系核样本上进行了演示。
Comments This manuscript was previously submitted to ICML for peer review. Reviewers noted that while the underlying VAE-based architecture builds on established methods, its application to spatially-resolved IFS data is promising for unsupervised representation learning in astronomy. This version is released for community visibility. Reviewer decisions: Weak accept and Weak reject (Final: Reject)
