In this work, we address the problem of automating quantum variational
machine learning. We develop a multi-locality parallelizable search algorithm,
called MUSE, to find the initial points and the sets of parameters that achieve
the best performance for quantum variational circuit learning. Simulations with
five real-world classification datasets indicate that on average, MUSE improves
the detection accuracy of quantum variational classifiers 2.3 times with
respect to the observed lowest scores. Moreover, when applied to two real-world
regression datasets, MUSE improves the quality of the predictions from negative
coefficients of determination to positive ones. Furthermore, the classification
and regression scores of the quantum variational models trained with MUSE are
on par with the classical counterparts.

我们提出了一种自动化量子变分机器学习的方法，称为 MUSE 算法，在五个真实世界的分类数据集上进行模拟实验，并通过改进量子变分分类器的检测准确性和预测质量，显示出与经典模型相当的分类和回归得分。

自动化的量子变分机器学习探索

Toward Automated Quantum Variational Machine Learning

Transformed Gaussian Processes (TGPs) are stochastic processes specified by
transforming samples from the joint distribution from a prior process
(typically a GP) using an invertible transformation; increasing the flexibility
of the base process.
Furthermore, they achieve competitive results compared with Deep Gaussian
Processes (DGPs), which are another generalization constructed by a
hierarchical concatenation of GPs. In this work, we propose a generalization of
TGPs named Deep Transformed Gaussian Processes (DTGPs), which follows the trend
of concatenating layers of stochastic processes. More precisely, we obtain a
multi-layer model in which each layer is a TGP. This generalization implies an
increment of flexibility with respect to both TGPs and DGPs. Exact inference in
such a model is intractable. However, we show that one can use variational
inference to approximate the required computations yielding a straightforward
extension of the popular DSVI inference algorithm Salimbeni et al (2017). The
experiments conducted evaluate the proposed novel DTGPs in multiple regression
datasets, achieving good scalability and performance.

通过变换样本中的联合分布，Deep Transformed Gaussian Processes（DTGPs）构建了多层模型，每一层都是转换高斯过程，增加了模型的灵活性和可扩展性。通过使用变分推断来近似计算，该方法扩展了 DSVI 推断算法，实验证明在多个回归数据集上取得了良好的性能。

深度转换高斯过程

Deep Transformed Gaussian Processes

Due to the high computational demands executing a rigorous comparison between
hyperparameter optimization (HPO) methods is often cumbersome. The goal of this
paper is to facilitate a better empirical evaluation of HPO methods by
providing benchmarks that are cheap to evaluate, but still represent realistic
use cases. We believe these benchmarks provide an easy and efficient way to
conduct reproducible experiments for neural hyperparameter search. Our
benchmarks consist of a large grid of configurations of a feed forward neural
network on four different regression datasets including architectural
hyperparameters and hyperparameters concerning the training pipeline. Based on
this data, we performed an in-depth analysis to gain a better understanding of
the properties of the optimization problem, as well as of the importance of
different types of hyperparameters. Second, we exhaustively compared various
different state-of-the-art methods from the hyperparameter optimization
literature on these benchmarks in terms of performance and robustness.

为了更好地评估超参数优化方法，本文提供了一组便宜但仍然代表现实用例的基准测试，并在基准测试上对超参数优化方法进行了广泛的性能和鲁棒性比较。