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.
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Question: We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our algorithm draws ideas from (1) isotonic regression for learning neural networks and (2) landscape analysis of non-convex matrix factorization problems. We believe these findings may inspire further development in designing provable algorithms for learning neural networks and other complex models. While our focus is theoretical, we also present experiments that illustrate our theoretical findings.

Answer: We propose an algorithm for provably recovering parameters (convolutional and output weights) of a convolutional network with overlapping patches.

Question: Recent image super-resolution(SR) studies leverage very deep convolutional neural networks and the rich hierarchical features they offered, which leads to better reconstruction performance than conventional methods. However, the small receptive fields in the up-sampling and reconstruction process of those models stop them to take full advantage of global contextual information. This causes problems for further performance improvement. In this paper, inspired by image reconstruction principles of human visual system, we propose an image super-resolution global reasoning network (SRGRN) to effectively learn the correlations between different regions of an image, through global reasoning. Specifically, we propose global reasoning up-sampling module (GRUM) and global reasoning reconstruction block (GRRB). They construct a graph model to perform relation reasoning on regions of low resolution (LR) images.They aim to reason the interactions between different regions in the up-sampling and reconstruction process and thus leverage more contextual information to generate accurate details. Our proposed SRGRN are more robust and can handle low resolution images that are corrupted by multiple types of degradation. Extensive experiments on different benchmark data-sets show that our model outperforms other state-of-the-art methods. Also our model is lightweight and consumes less computing power, which makes it very suitable for real life deployment.

Answer: A state-of-the-art model based on global reasoning for image super-resolution

Question: Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.
Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.
Furthermore, social networks can be extracted from email corpora, tweets, or social media.
When it comes to visualising these large corpora, either the textual content or the network graph are used.

In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.
To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.
We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.

Answer:
Dimensionality reduction algorithm to visualise text with network information, for example an email corpus or co-authorships.