In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Q: Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques.

A: A unified statistical view of the broad class of deep generative models
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Q: In colored graphs, node classes are often associated with either their neighbors class or with information not incorporated in the graph associated with each node. We here propose that node classes are also associated with topological features of the nodes. We use this association to improve Graph machine learning in general and specifically, Graph Convolutional Networks (GCN).

First, we show that even in the absence of any external information on nodes, a good accuracy can be obtained on the prediction of the node class using either topological features, or using the neighbors class as an input to a GCN. This accuracy is slightly less than the one that can be obtained using content based GCN.

Secondly, we show that explicitly adding the topology as an input to the GCN does not improve the accuracy when combined with external information on nodes. However, adding an additional adjacency matrix with edges between distant nodes with similar topology to the GCN does significantly improve its accuracy, leading to results better than all state of the art methods in multiple datasets.

A: Topology-Based Graph Convolutional Network (GCN)
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Q: Deep neural networks have achieved state-of-the-art performance in various fields, but they have to be scaled down to be used for real-world applications. As a means to reduce the size of a neural network while preserving its performance, knowledge transfer has brought a lot of attention. One popular method of knowledge transfer is knowledge distillation (KD), where softened outputs of a pre-trained teacher network help train student networks. Since KD, other transfer methods have been proposed, and they mainly focus on loss functions, activations of hidden layers, or additional modules to transfer knowledge well from teacher networks to student networks. In this work, we focus on the structure of a teacher network to get the effect of multiple teacher networks without additional resources. We propose changing the structure of a teacher network to have stochastic blocks and skip connections. In doing so, a teacher network becomes the aggregate of a huge number of paths. In the training phase, each sub-network is generated by dropping stochastic blocks randomly and used as a teacher network. This allows training the student network with multiple teacher networks and further enhances the student network on the same resources in a single teacher network. We verify that the proposed structure brings further improvement to student networks on benchmark datasets.

A:
The goal of this paper is to get the effect of multiple teacher networks by exploiting stochastic blocks and skip connections.
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