论文标题
并行的层次分类的树方法
Tree Methods for Hierarchical Classification in Parallel
论文作者
论文摘要
我们提出的方法可以并行启用有效的分层分类。我们的方法将与语义树中给定的节点相对应的一批分类分数和标签转换为与对应于祖先路径中所有节点相对应的分数和标签，仅依赖于在硬件加速器上有效地执行的张量操作。我们在当前的硬件加速器上实现了我们的方法，并用一棵树结合了WordNet 3.0中的所有英语综合体，跨越20级的深度，涵盖117,659个类。我们将一批分数和标签转换为各自的祖先路径，产生可忽略不计的计算，并且仅在数据足迹上消耗了0.04GB的内存。
We propose methods that enable efficient hierarchical classification in parallel. Our methods transform a batch of classification scores and labels, corresponding to given nodes in a semantic tree, to scores and labels corresponding to all nodes in the ancestral paths going down the tree to every given node, relying only on tensor operations that execute efficiently on hardware accelerators. We implement our methods and test them on current hardware accelerators with a tree incorporating all English-language synsets in WordNet 3.0, spanning 117,659 classes in 20 levels of depth. We transform batches of scores and labels to their respective ancestral paths, incurring negligible computation and consuming only a fixed 0.04GB of memory over the footprint of data.
