Motivated by the needs from an airline crew scheduling application, we
introduce structured convolutional kernel networks (Struct-CKN), which combine
CKNs from Mairal et al. (2014) in a structured prediction framework that
supports constraints on the outputs. CKNs are a particular kind of
convolutional neural networks that approximate a kernel feature map on training
data, thus combining properties of deep learning with the non-parametric
flexibility of kernel methods. Extending CKNs to structured outputs allows us
to obtain useful initial solutions on a flight-connection dataset that can be
further refined by an airline crew scheduling solver. More specifically, we use
a flight-based network modeled as a general conditional random field capable of
incorporating local constraints in the learning process. Our experiments
demonstrate that this approach yields significant improvements for the
large-scale crew pairing problem (50,000 flights per month) over standard
approaches, reducing the solution cost by 17% (a gain of millions of dollars)
and the cost of global constraints by 97%.

引入了结构卷积核网络（Struct-CKN）作为一种支持输出约束的结构化预测框架，将 Mairal 等人的 CKN 结合起来，将 CKNs 推广到了结构化输出，通过一个基于航班的网络建模实现了航空公司机组人员调度问题的初始解。实验证明，这种方法在标准方法上取得了重大改进，降低了解决方案成本 17％，全局约束成本降低了 97％。

基于结构化卷积核网络的航空公司机组排班

Structured Convolutional Kernel Networks for Airline Crew Scheduling

The configurational information in sentences of a free word order language
such as Sanskrit is of limited use. Thus, the context of the entire sentence
will be desirable even for basic processing tasks such as word segmentation. We
propose a structured prediction framework that jointly solves the word
segmentation and morphological tagging tasks in Sanskrit. We build an energy
based model where we adopt approaches generally employed in graph based parsing
techniques (McDonald et al., 2005a; Carreras, 2007). Our model outperforms the
state of the art with an F-Score of 96.92 (percentage improvement of 7.06%)
while using less than one-tenth of the task-specific training data. We find
that the use of a graph based ap- proach instead of a traditional lattice-based
sequential labelling approach leads to a percentage gain of 12.6% in F-Score
for the segmentation task.

本研究提出了一种在梵文中结合了词语分割和形态标注任务的结构化预测框架，采用了图形解析技术中通常采用的方法，其在使用不到特定训练数据的情况下，F 值达到了 96.92%（改善率为 7.06%），而采用图形解析方法而不是传统基于格子的顺序标注方法，则在分割任务中导致 F 值提高了 12.6%。