论文标题
部分可观测时空混沌系统的无模型预测
Bayesian Optimization of Sampling Densities in MRI
论文作者
论文摘要
在MRI中，数据驱动的采样模式的优化最近受到了极大的关注。在离网优化中，对最小化的最小化组合数的最新观察结果进行了关注，我们提出了一个框架，以使用贝叶斯优化的方式在全球范围内优化采样密度。使用降低技术，我们优化采样轨迹的速度比传统的离网方法快20倍以上，并具有限制数量的训练样本。这种方法 - 除其他好处外，还要放弃自动差异的需求。它的性能比最先进的轨迹稍差，因为它降低了可接受的轨迹的空间，但具有显着的计算优势，但其其他贡献包括：i）仔细评估概率距离的距离，对基于轨迹的特定型号和不稳定的轨迹的培训ii）ii）ii）ii）ii）ii）ii）ii）ii tragistion II）ii）ii tragistion ii tragistion ii trajection ii tragistion II）a） 优化。
Data-driven optimization of sampling patterns in MRI has recently received a significant attention.Following recent observations on the combinatorial number of minimizers in off-the-grid optimization, we propose a framework to globally optimize the sampling densities using Bayesian optimization. Using a dimension reduction technique, we optimize the sampling trajectories more than 20 times faster than conventional off-the-grid methods, with a restricted number of training samples. This method -- among other benefits -- discards the need of automatic differentiation.Its performance is slightly worse than state-of-the-art learned trajectories since it reduces the space of admissible trajectories, but comes with significant computational advantages.Other contributions include: i) a careful evaluation of the distance in probability space to generate trajectories ii) a specific training procedure on families of operators for unrolled reconstruction networks and iii) a gradient projection based scheme for trajectory optimization.
