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similarity [2018/10/10 11:52]
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similarity [2018/11/03 12:25] (current)
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 Our key insight is to define a distance based on the long term diffusion behavior of the whole network. We first introduce a dynamic system on graphs called Laplacian flow. Based on this Laplacian flow, a new version of diffusion distance between networks is proposed. We will demonstrate the utility of the distance and its advantage over various existing distances through explicit examples. The distance is also applied to subsequent learning tasks such as clustering network objects. Our key insight is to define a distance based on the long term diffusion behavior of the whole network. We first introduce a dynamic system on graphs called Laplacian flow. Based on this Laplacian flow, a new version of diffusion distance between networks is proposed. We will demonstrate the utility of the distance and its advantage over various existing distances through explicit examples. The distance is also applied to subsequent learning tasks such as clustering network objects.
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 +https://​arxiv.org/​pdf/​1810.13337v1.pdf LEARNING TO REPRESENT EDITS
 +
 +By combining
 +a “neural editor” with an “edit encoder”, our models learn to represent the
 +salient information of an edit and can be used to apply edits to new inputs. We
 +experiment on natural language and source code edit data. 
 +
 +https://​arxiv.org/​abs/​1808.10584 Learning to Describe Differences Between Pairs of Similar Images
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 +We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. ​