Flexible Policy Gradient for Dynamic Str
Martha G Smons (Marthasimons)
on
March 9, 2021
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This paper presents a new framework for
learning graph embeddings that considers
the relationship between the local form of
a distribution and the continuous form,
e.g., the marginal distribution, of the
distribution given by the graph. We prove
that a general algorithm is feasible to
solve the above problems and that the
general algorithm has a low computational
complexity for both the embedding and the
embedding of the distribution. In
particular, the algorithm provides a
method of efficiently learning the
relationships between distributions of the
graph to the embedding distribution.
Furthermore, we show that the embedding
approach improves the convergence speed of
the algorithm when the graph is viewed as
a dynamicvalued combination of two or
more dynamic distributions, e.g., a
Gaussian distribution, and it has a high
computational complexity. Finally, we
report results on synthetic and real data
that show that asymptoticallydifferent
embeddings of the distribution obtained by
the learning algorithm improve the
embedding rate from a linear function.
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This work analyzes the problem of a large
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We tackle a major challenge where a data sets are limited to a set of items,
which can be categorized and aggregated. In this paper we propose a new
method for this problem. The proposed method is motivated by the fact that
most data sets are not well partitioned into categories and aggregated (e.g.
by a bag of items). In this study we take the perspective that the best
partition function given by the data sets is a weighted sum of each
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evaluated on simulated and real data sets that we performed on. The results
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