2020年10月14日 · In this paper, instead of training high-dimensional models, we propose MulDE, a novel knowledge distillation framework, which includes multiple low-dimensional hyperbolic …

The Mulde (German pronunciation: ⓘ) is a river in Saxony and Saxony-Anhalt, Germany. It is a left tributary of the Elbe and is 124 kilometres (77 mi) long. The river is formed by the …

In this paper, instead of training high-dimensional models, we propose MulDE, a novel knowledge distillation frame- work, which includes multiple low-dimensional hyperbolic KGE models as …

2021年10月19日 · 论文介绍了一种名为MulDE的新方法，旨在解决低维知识图嵌入的问题。MulDE通过多教师知识蒸馏，避免了高维模型的训练成本和存储需求，同时保持了知识准确性。

2020年10月14日 · In this paper, instead of training high-dimensional models, we propose MulDE, a novel knowledge distillation framework, which includes multiple low-dimensional hyperbolic …

本文提出了一种新的面向低维知识图嵌入的多教师知识蒸馏方法MulDE来解决这个问题。 选择使用多个低维度（64维）教师有以下3个好处： 1. 降低预训练成本； 2. 能保证教师的性能； 3. 提 …

2021年4月19日 · MulDE (Wang et al. 2021b) applies the knowledge distillation technology to transfer the knowledge from multiple low-dimensional hyperbolic KGE teacher models to a …

MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings 课程网址: http://videolectures.net/www2021_wang_multiteacher_knowledge/

The Multi-teacher Distillation Embedding (MulDE) model utilizes multiple pre-trained low-dimensional models as teachers, extracts knowledge by contrasting different teacher scores, …

This is the official PyTorch implementation of the density-based anomaly detector "MULDE" which is trained via score matching. The anomaly detector is proposed in the paper MULDE: …