Kazuki Fukui

most citedRepresentation Learning with Weighted Inner Product for Universal Approximation of General Similarities

2 citations · 2 across the 1 of their papers we have counts for

collaborators

2 papers

cs.LG2019★ 2 cited

Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities

Geewook Kim, Akifumi Okuno, Kazuki Fukui +1

We propose $weighted inner product similarity$ (WIPS) for neural network-based graph embedding. In addition to the parameters of neural networks, we optimize the weights o…

cs.CL2018

Segmentation-free Compositional $n$ -gram Embedding

Geewook Kim, Kazuki Fukui, Hidetoshi Shimodaira

We propose a new type of representation learning method that models words, phrases and sentences seamlessly. Our method does not depend on word segmentation and any human-annotated…

Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities

Segmentation-free Compositional n-gram Embedding

Segmentation-free Compositional $n$ -gram Embedding