13 citations · 33 across the 25 of their papers we have counts for
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cs.SI2019
Hyperlink Regression via Bregman Divergence
Akifumi Okuno, Hidetoshi Shimodaira
A collection of data vectors is called a -tuple, and the association strength among the vectors of a tuple is termed as the \emph{hyperlink weight}, that…
stat.ML2019★ 5 cited
Robust Graph Embedding with Noisy Link Weights
Akifumi Okuno, Hidetoshi Shimodaira
We propose -graph embedding for robustly learning feature vectors from data vectors and noisy link weights. A newly introduced empirical moment -score reduces the influence o…
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 (WIPS) for neural network-based graph embedding. In addition to the parameters of neural networks, we optimize the weights o…