Inflation is a highly favoured theory for the early Universe. It is
compatible with current observations of the cosmic microwave background and
large scale structure and is a driver in the quest to detect primordial
gravitational waves. It is also, given the current quality of the data, highly
under-determined with a large number of candidate implementations. We use a new
method in symbolic regression to generate all possible simple scalar field
potentials for one of two possible basis sets of operators. Treating these as
single-field, slow-roll inflationary models we then score them with an
information-theoretic metric ("minimum description length") that quantifies
their efficiency in compressing the information in the Planck data. We explore
two possible priors on the parameter space of potentials, one related to the
functions' structural complexity and one that uses a Katz back-off language
model to prefer functions that may be theoretically motivated. This enables us
to identify the inflaton potentials that optimally balance simplicity with
accuracy at explaining the Planck data, which may subsequently find theoretical
motivation. Our exploratory study opens the door to extraction of fundamental
physics directly from data, and may be augmented with more refined theoretical
priors in the quest for a complete understanding of the early Universe.

利用符号回归的新方法生成了所有可能的标量场势，通过使用信息论度量方法（“最小描述长度”）评估其在压缩 Planck 数据中的信息效率，以找到平衡简单性与准确性的膨胀势，进而从数据中直接提取基本物理并加以理论推导，以完全理解早期宇宙。

最简单的通胀势

The Simplest Inflationary Potentials

We study the problem of unsupervised domain adaptation, which aims to adapt
classifiers trained on a labeled source domain to an unlabeled target domain.
Many existing approaches first learn domain-invariant features and then
construct classifiers with them. We propose a novel approach that jointly learn
the both. Specifically, while the method identifies a feature space where data
in the source and the target domains are similarly distributed, it also learns
the feature space discriminatively, optimizing an information-theoretic metric
as an proxy to the expected misclassification error on the target domain. We
show how this optimization can be effectively carried out with simple
gradient-based methods and how hyperparameters can be cross-validated without
demanding any labeled data from the target domain. Empirical studies on
benchmark tasks of object recognition and sentiment analysis validated our
modeling assumptions and demonstrated significant improvement of our method
over competing ones in classification accuracies.

该文章研究了无监督领域自适应的问题，提出一种新的方法，该方法同时学习 domain-invariant features 和 discriminative features，并且以信息论度量作为优化目标，在目标域和源域中训练分类器。经过实验验证，该方法在物体识别和情感分析等任务中表现出了明显的优势。