13 citations · 33 across the 25 of their papers we have counts for
3 papers · 1 filter
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira
We propose shifted inner-product similarity (SIPS), which is a novel yet very simple extension of the ordinary inner-product similarity (IPS) for neural-network based graph embeddi…
On representation power of neural network-based graph embedding and beyond
Akifumi Okuno, Hidetoshi Shimodaira
We consider the representation power of siamese-style similarity functions used in neural network-based graph embedding. The inner product similarity (IPS) with feature vectors com…
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira
A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is proposed for multi-view feature learning with many-to-many associations so that it generalizes various existi…