A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings
Katsuhiko Hayashi, Koki Kishimoto, Masashi Shimbo · 2020
This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP.Unlike the prior work using a SGDbased method and quantization of real-valued vectors, the proposed method directly optimizes binary embedding vectors by a series of bit flipping operations.On the standard knowledge graph completion tasks, the B-CP model trained with the proposed method achieved comparable performance with that trained with SGD as well as state-of-the-art real-valued models with similar embedding dimensions.