Efficient load forecasting is needed to ensure better observability in the
distribution networks, whereas such forecasting is made possible by an
increasing number of smart meter installations. Because distribution networks
include a large amount of different loads at various aggregation levels, such
as individual consumers, transformer stations and feeders loads, it is
impractical to develop individual (or so-called local) forecasting models for
each load separately. Furthermore, such local models ignore the strong
dependencies between different loads that might be present due to their spatial
proximity and the characteristics of the distribution network. To address these
issues, this paper proposes a global modeling approach based on deep learning
for efficient forecasting of a large number of loads in distribution networks.
In this way, the computational burden of training a large amount of local
forecasting models can be largely reduced, and the cross-series information
shared among different loads can be utilized. Additionally, an unsupervised
localization mechanism and optimal ensemble construction strategy are also
proposed to localize/personalize the forecasting model to different groups of
loads and to improve the forecasting accuracy further. Comprehensive
experiments are conducted on real-world smart meter data to demonstrate the
superiority of the proposed approach compared to competing methods.

该研究提出了一种基于深度学习的全局建模方法、无监督的本地化机制和最优集成构建策略，以有效预测分布式网络中大量负载，该方法通过减少模型训练的计算负担，以及利用不同负载之间的交叉信息共享，能够比竞争方法更好地适用于实际智能电表数据，并提高预测精度。

配电网负荷预测的全球建模方法

A Global Modeling Approach for Load Forecasting in Distribution Networks

It is well recognized that the project productivity is a key driver in
estimating software project effort from Use Case Point size metric at early
software development stages. Although, there are few proposed models for
predicting productivity, there is no consistent conclusion regarding which
model is the superior. Therefore, instead of building a new productivity
prediction model, this paper presents a new ensemble construction mechanism
applied for software project productivity prediction. Ensemble is an effective
technique when performance of base models is poor. We proposed a weighted mean
method to aggregate predicted productivities based on average of errors
produced by training model. The obtained results show that the using ensemble
is a good alternative approach when accuracies of base models are not
consistently accurate over different datasets, and when models behave
diversely.

本文探讨了一种新的用于软件项目生产力预测的集成构建机制，利用带权平均方法来汇聚基模型的预测结果，结果表明基模型准确性不一致时，使用集成方法是一种良好的替代方案。

基于用例点的软件工作量学习项目生产力集成

Ensemble of Learning Project Productivity in Software Effort Based on  Use Case Points

One of the goals of the ICML workshop on representation and learning is to
establish benchmark scores for a new data set of labeled facial expressions.
This paper presents the performance of a "Null" model consisting of
convolutions with random weights, PCA, pooling, normalization, and a linear
readout. Our approach focused on hyperparameter optimization rather than novel
model components. On the Facial Expression Recognition Challenge held by the
Kaggle website, our hyperparameter optimization approach achieved a score of
60% accuracy on the test data. This paper also introduces a new ensemble
construction variant that combines hyperparameter optimization with the
construction of ensembles. This algorithm constructed an ensemble of four
models that scored 65.5% accuracy. These scores rank 12th and 5th respectively
among the 56 challenge participants. It is worth noting that our approach was
developed prior to the release of the data set, and applied without
modification; our strong competition performance suggests that the TPE
hyperparameter optimization algorithm and domain expertise encoded in our Null
model can generalize to new image classification data sets.

该研究论文介绍了一种使用超参数优化和集成构建方法来识别面部表情的算法，该算法在新的数据集上实现了很高的分类准确率，并且在 Kaggel 竞赛的排名也优秀。