9 citations · 21 across the 26 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2024
Multi-label Learning with Random Circular Vectors
Ken Nishida, Kojiro Machi, Kazuma Onishi +2
The extreme multi-label classification~(XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance. Whi…
cs.LG2022★ 9 cited
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
Hidetaka Kamigaito, Katsuhiko Hayashi
Negative sampling (NS) loss plays an important role in learning knowledge graph embedding (KGE) to handle a huge number of entities. However, the performance of KGE degrades withou…
cs.LG2021★ 9 cited
Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding
Hidetaka Kamigaito, Katsuhiko Hayashi
In knowledge graph embedding, the theoretical relationship between the softmax cross-entropy and negative sampling loss functions has not been investigated. This makes it difficult…