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.

[EX Q]: Convolutional Neural Networks (CNNs) are composed of multiple convolution layers and show elegant performance in vision tasks.
 The design of the regular convolution is based on the Receptive Field (RF) where the information within a specific region is processed.
 In the view of the regular convolution's RF, the outputs of neurons in lower layers with smaller RF are bundled to create neurons in higher layers with larger RF. 
 As a result, the neurons in high layers are able to capture the global context even though the neurons in low layers only see the local information.
 However, in lower layers of the biological brain, the information outside of the RF changes the properties of neurons.
 In this work, we extend the regular convolution and propose spatially shuffled convolution (ss convolution).
 In ss convolution, the regular convolution is able to use the information outside of its RF by spatial shuffling which is a simple and lightweight operation.
 We perform experiments on CIFAR-10 and ImageNet-1k dataset, and show that ss convolution improves the classification performance across various CNNs.
[EX A]: We propose spatially shuffled convolution that the regular convolution incorporates the information from outside of its receptive field.

[EX Q]: The available resolution in our visual world is extremely high, if not infinite. Existing CNNs can be applied in a fully convolutional way to images of arbitrary resolution, but as the size of the input increases, they can not capture contextual information. In addition, computational requirements scale linearly to the number of input pixels, and resources are allocated uniformly across the input, no matter how informative different image regions are. We attempt to address these problems by proposing a novel architecture that traverses an image pyramid in a top-down fashion, while it uses a hard attention mechanism to selectively process only the most informative image parts. We conduct experiments on MNIST and ImageNet datasets, and we show that our models can significantly outperform fully convolutional counterparts, when the resolution of the input is that big that the receptive field of the baselines can not adequately cover the objects of interest. Gains in performance come for less FLOPs, because of the selective processing that we follow. Furthermore, our attention mechanism makes our predictions more interpretable, and creates a trade-off between accuracy and complexity that can be tuned both during training and testing time.
[EX A]: We propose a novel architecture that traverses an image pyramid in a top-down fashion, while it visits only the most informative regions along the way.

[EX Q]: Recent results from linear algebra stating that any matrix can be decomposed into products of diagonal and circulant matrices has lead to the design of compact deep neural network architectures that perform well in practice. In this paper, we bridge the gap between these good empirical results 
 and the theoretical approximation capabilities of Deep diagonal-circulant ReLU networks. More precisely, we first demonstrate  that a Deep diagonal-circulant ReLU networks of
 bounded width and small depth can approximate a deep ReLU network in which the dense matrices are
 of low rank. Based on this result, we provide new bounds on the expressive power and universal approximativeness of this type of networks. We support our experimental results with thorough experiments on a large, real world video classification problem.
[EX A]:
We provide a theoretical study of the properties of Deep circulant-diagonal ReLU Networks and demonstrate that they are bounded width universal approximators.