19,558 research outputs found
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
We present a method that learns to integrate temporal information, from a
learned dynamics model, with ambiguous visual information, from a learned
vision model, in the context of interacting agents. Our method is based on a
graph-structured variational recurrent neural network (Graph-VRNN), which is
trained end-to-end to infer the current state of the (partially observed)
world, as well as to forecast future states. We show that our method
outperforms various baselines on two sports datasets, one based on real
basketball trajectories, and one generated by a soccer game engine.Comment: ICLR 2019 camera read
Revisiting several problems and algorithms in continuous location with lp norms
This paper addresses the general continuous single facility location
problems in finite dimension spaces under possibly different â„“p norms
in the demand points. We analyze the difficulty of this family of problems
and revisit convergence properties of some well-known algorithms.
The ultimate goal is to provide a common approach to solve the family
of continuous â„“p ordered median location problems in dimension d (including
of course the â„“p minisum or Fermat-Weber location problem
for any p ≥ 1). We prove that this approach has a polynomial worse
case complexity for monotone lambda weights and can be also applied
to constrained and even non-convex problems.Junta de AndalucÃaFondo Europeo de Desarrollo RegionalMinisterio de Ciencia e Innovació
A Semidefinite Programming approach for minimizing ordered weighted averages of rational functions
This paper considers the problem of minimizing the ordered weighted average
(or ordered median) function of finitely many rational functions over compact
semi-algebraic sets. Ordered weighted averages of rational functions are not,
in general, neither rational functions nor the supremum of rational functions
so that current results available for the minimization of rational functions
cannot be applied to handle these problems. We prove that the problem can be
transformed into a new problem embedded in a higher dimension space where it
admits a convenient representation. This reformulation admits a hierarchy of
SDP relaxations that approximates, up to any degree of accuracy, the optimal
value of those problems. We apply this general framework to a broad family of
continuous location problems showing that some difficult problems (convex and
non-convex) that up to date could only be solved on the plane and with
Euclidean distance, can be reasonably solved with different -norms and
in any finite dimension space. We illustrate this methodology with some
extensive computational results on location problems in the plane and the
3-dimension space.Comment: 27 pages, 1 figure, 7 table
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