The performance of modern machine learning algorithms depends upon the
selection of a set of hyperparameters. Common examples of hyperparameters are
learning rate and the number of layers in a dense neural network. Auto-ML is a
branch of optimization that has produced important contributions in this area.
Within Auto-ML, hyperband-based approaches, which eliminate poorly-performing
configurations after evaluating them at low budgets, are among the most
effective. However, the performance of these algorithms strongly depends on how
effectively they allocate the computational budget to various hyperparameter
configurations. We present the new Parameter Optimization with Conscious
Allocation (POCA), a hyperband-based algorithm that adaptively allocates the
inputted budget to the hyperparameter configurations it generates following a
Bayesian sampling scheme. We compare POCA to its nearest competitor at
optimizing the hyperparameters of an artificial toy function and a deep neural
network and find that POCA finds strong configurations faster in both settings.

现代机器学习算法的性能取决于一组超参数的选择。Auto-ML 是优化的一个分支，在这一领域中，基于超带宽的方法是最有效的，而 Parameter Optimization with Conscious Allocation (POCA) 是一种自适应地将预算分配给超参数配置的超带宽算法，通过贝叶斯抽样方案生成的超参数配置更快地找到强配置。

意识分配参数优化 (POCA)

Parameter Optimization with Conscious Allocation (POCA)

Recently deep learning based quantitative structure-activity relationship
(QSAR) models has shown surpassing performance than traditional methods for
property prediction tasks in drug discovery. However, most DL based QSAR models
are restricted to limited labeled data to achieve better performance, and also
are sensitive to model scale and hyper-parameters. In this paper, we propose
Uni-QSAR, a powerful Auto-ML tool for molecule property prediction tasks.
Uni-QSAR combines molecular representation learning (MRL) of 1D sequential
tokens, 2D topology graphs, and 3D conformers with pretraining models to
leverage rich representation from large-scale unlabeled data. Without any
manual fine-tuning or model selection, Uni-QSAR outperforms SOTA in 21/22 tasks
of the Therapeutic Data Commons (TDC) benchmark under designed parallel
workflow, with an average performance improvement of 6.09\%. Furthermore, we
demonstrate the practical usefulness of Uni-QSAR in drug discovery domains.

该论文提出了一个名为 Uni-QSAR 的深度学习自动机器学习工具，结合了分子表示学习和预训练模型，利用大规模无标签数据得到丰富的表示，优于 SOTA 并在药物发现领域具有实际用途。