15,140 research outputs found
Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a
composable architecture for robot learning systems. A DAN is composed of neural
network modules, each encoding a differentiable robot algorithm and an
associated model; and it is trained end-to-end from data. DAN combines the
strengths of model-driven modular system design and data-driven end-to-end
learning. The algorithms and models act as structural assumptions to reduce the
data requirements for learning; end-to-end learning allows the modules to adapt
to one another and compensate for imperfect models and algorithms, in order to
achieve the best overall system performance. We illustrate the DAN methodology
through a case study on a simulated robot system, which learns to navigate in
complex 3-D environments with only local visual observations and an image of a
partially correct 2-D floor map.Comment: RSS 2019 camera ready. Video is available at
https://youtu.be/4jcYlTSJF4
Software Engineering and Complexity in Effective Algebraic Geometry
We introduce the notion of a robust parameterized arithmetic circuit for the
evaluation of algebraic families of multivariate polynomials. Based on this
notion, we present a computation model, adapted to Scientific Computing, which
captures all known branching parsimonious symbolic algorithms in effective
Algebraic Geometry. We justify this model by arguments from Software
Engineering. Finally we exhibit a class of simple elimination problems of
effective Algebraic Geometry which require exponential time to be solved by
branching parsimonious algorithms of our computation model.Comment: 70 pages. arXiv admin note: substantial text overlap with
arXiv:1201.434
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