We present a unified framework called deep dependency networks (DDNs) that
combines dependency networks and deep learning architectures for multi-label
classification, with a particular emphasis on image and video data. The primary
advantage of dependency networks is their ease of training, in contrast to
other probabilistic graphical models like Markov networks. In particular, when
combined with deep learning architectures, they provide an intuitive,
easy-to-use loss function for multi-label classification. A drawback of DDNs
compared to Markov networks is their lack of advanced inference schemes,
necessitating the use of Gibbs sampling. To address this challenge, we propose
novel inference schemes based on local search and integer linear programming
for computing the most likely assignment to the labels given observations. We
evaluate our novel methods on three video datasets (Charades, TACoS, Wetlab)
and three image datasets (MS-COCO, PASCAL VOC, NUS-WIDE), comparing their
performance with (a) basic neural architectures and (b) neural architectures
combined with Markov networks equipped with advanced inference and learning
techniques. Our results demonstrate the superiority of our new DDN methods over
the two competing approaches.

我们提出了一个统一的框架，称为深层依赖网络（DDN），它将依赖网络和深度学习架构结合起来，用于多标签分类，特别是在图像和视频数据上。

深度依赖网络和高级推理方案在多标签分类中的应用

Deep Dependency Networks and Advanced Inference Schemes for Multi-Label  Classification

We present a new data analysis perspective to determine variable importance
regardless of the underlying learning task. Traditionally, variable selection
is considered an important step in supervised learning for both classification
and regression problems. The variable selection also becomes critical when
costs associated with the data collection and storage are considerably high for
cases like remote sensing. Therefore, we propose a new methodology to select
important variables from the data by first creating dependency networks among
all variables and then ranking them (i.e. nodes) by graph centrality measures.
Selecting Top-$n$ variables according to preferred centrality measure will
yield a strong candidate subset of variables for further learning tasks e.g.
clustering. We present our tool as a Shiny app which is a user-friendly
interface development environment. We also extend the user interface for two
well-known unsupervised variable selection methods from literature for
comparison reasons.

提出了一种基于依赖网络和图中心性度量实现变量重要性评估的新数据分析方法，该方法无需考虑底层学习任务类型，通过选择排名前 n 位的图中心性量度高的变量来构建备选变量集合，可为进一步的学习任务提供强有力的变量子集。同时，利用 Shiny app 开发了一个用户友好的工具，方便选择和比较变量子集。