44,491 research outputs found
Injecting External Solutions Into CMA-ES
This report considers how to inject external candidate solutions into the
CMA-ES algorithm. The injected solutions might stem from a gradient or a Newton
step, a surrogate model optimizer or any other oracle or search mechanism. They
can also be the result of a repair mechanism, for example to render infeasible
solutions feasible. Only small modifications to the CMA-ES are necessary to
turn injection into a reliable and effective method: too long steps need to be
tightly renormalized. The main objective of this report is to reveal this
simple mechanism. Depending on the source of the injected solutions,
interesting variants of CMA-ES arise. When the best-ever solution is always
(re-)injected, an elitist variant of CMA-ES with weighted multi-recombination
arises. When \emph{all} solutions are injected from an \emph{external} source,
the resulting algorithm might be viewed as \emph{adaptive encoding} with
step-size control. In first experiments, injected solutions of very good
quality lead to a convergence speed twice as fast as on the (simple) sphere
function without injection. This means that we observe an impressive speed-up
on otherwise difficult to solve functions. Single bad injected solutions on the
other hand do no significant harm.Comment: No. RR-7748 (2011
Diversified Texture Synthesis with Feed-forward Networks
Recent progresses on deep discriminative and generative modeling have shown
promising results on texture synthesis. However, existing feed-forward based
methods trade off generality for efficiency, which suffer from many issues,
such as shortage of generality (i.e., build one network per texture), lack of
diversity (i.e., always produce visually identical output) and suboptimality
(i.e., generate less satisfying visual effects). In this work, we focus on
solving these issues for improved texture synthesis. We propose a deep
generative feed-forward network which enables efficient synthesis of multiple
textures within one single network and meaningful interpolation between them.
Meanwhile, a suite of important techniques are introduced to achieve better
convergence and diversity. With extensive experiments, we demonstrate the
effectiveness of the proposed model and techniques for synthesizing a large
number of textures and show its applications with the stylization.Comment: accepted by CVPR201
Blind deconvolution of sparse pulse sequences under a minimum distance constraint: a partially collapsed Gibbs sampler method
For blind deconvolution of an unknown sparse sequence convolved with an unknown pulse, a powerful Bayesian method employs the Gibbs sampler in combination with a BernoulliâGaussian prior modeling sparsity. In this paper, we extend this method by introducing a minimum distance constraint for the pulses in the sequence. This is physically relevant in applications including layer detection, medical imaging, seismology, and multipath parameter estimation. We propose a Bayesian method for blind deconvolution that is based on a modified BernoulliâGaussian prior including a minimum distance constraint factor. The core of our method is a partially collapsed Gibbs sampler (PCGS) that tolerates and even exploits the strong local dependencies introduced by the minimum distance constraint. Simulation results demonstrate significant performance gains compared to a recently proposed PCGS. The main advantages of the minimum distance constraint are a substantial reduction of computational complexity and of the number of spurious components in the deconvolution result
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