We present a learning based framework for mesh quality improvement on
unstructured triangular and quadrilateral meshes. Our model learns to improve
mesh quality according to a prescribed objective function purely via self-play
reinforcement learning with no prior heuristics. The actions performed on the
mesh are standard local and global element operations. The goal is to minimize
the deviation of the node degrees from their ideal values, which in the case of
interior vertices leads to a minimization of irregular nodes.

通过自我对弈强化学习，我们提出了一个基于学习的框架，用于改善非结构化三角形和四边形网格的质量，目标是通过标准的局部和全局元素操作，最小化节点度数偏离其理想值，以达到减少不规则节点的目标。

用于多边形块分解的网格拓扑操作学习

Learning topological operations on meshes with application to block  decomposition of polygons

Hyperplane hashing aims at rapidly searching nearest points to a hyperplane,
and has shown practical impact in scaling up active learning with SVMs.
Unfortunately, the existing randomized methods need long hash codes to achieve
reasonable search accuracy and thus suffer from reduced search speed and large
memory overhead. To this end, this paper proposes a novel hyperplane hashing
technique which yields compact hash codes. The key idea is the bilinear form of
the proposed hash functions, which leads to higher collision probability than
the existing hyperplane hash functions when using random projections. To
further increase the performance, we propose a learning based framework in
which the bilinear functions are directly learned from the data. This results
in short yet discriminative codes, and also boosts the search performance over
the random projection based solutions. Large-scale active learning experiments
carried out on two datasets with up to one million samples demonstrate the
overall superiority of the proposed approach.

本文提出了一种基于双线性函数的超平面哈希技术，可以解决使用随机投影时现有随机方法所需的较长哈希编码导致降低搜索速度和内存占用等问题，并通过数据学习构建了一种短而有辨别力的哈希编码。实验表明，该方法在大规模主动学习任务中具有优越性。