To the best of our knowledge, this is the first application of deep learning for the problem of document dating.
From online matchmaking and dating sites, to medical residency placement programs, matching algorithms are used in areas spanning scheduling, planning, pairing of vertices, and network flows.
More specifically, matching strategies are very useful in flow network algorithms such as the Ford-Fulkerson algorithm and the Edmonds-Karp algorithm.
Graph matching problems generally consist of making connections within graphs using edges that do not share common vertices, such as pairing students in a class according to their respective qualifications; or it may consist of creating a bipartite matching, where two subsets of vertices are distinguished and each vertex in one subgroup must be matched to a vertex in another subgroup.
Bipartite matching is used, for example, to match men and women on a dating site.
Graph matching algorithms often use specific properties in order to identify sub-optimal areas in a matching, where improvements can be made to reach a desired goal.
Two famous properties are called augmenting paths and alternating paths, which are used to quickly determine whether a graph contains a maximum, or minimum, matching, or the matching can be further improved.
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The NBER does not define a recession in terms of two consecutive quarters of decline in real GDP.