This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
similarity [2018/08/19 03:23]
similarity [2018/11/03 12:25] (current)
Line 206: Line 206:
 https://​www.quantamagazine.org/​universal-method-to-sort-complex-information-found-20180813 https://​www.quantamagazine.org/​universal-method-to-sort-complex-information-found-20180813
 +https://​arxiv.org/​pdf/​1808.07526.pdf Deep Neural Network Structures Solving Variational Inequalities∗
 +We propose a novel theoretical framework to investigate deep neural networks using the
 +formalism of proximal fixed point methods for solving variational inequalities. We first show that
 +almost all activation functions used in neural networks are actually proximity operators. This leads
 +to an algorithmic model alternating firmly nonexpansive and linear operators. We derive new results
 +on averaged operator iterations to establish the convergence of this model, and show that the limit
 +of the resulting algorithm is a solution to a variational inequality
 +https://​arxiv.org/​abs/​1810.02906v1 Network Distance Based on Laplacian Flows on Graphs
 +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.
 +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
 +We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. ​