5 citations · 6 across the 3 of their papers we have counts for
3 papers
Compositionality-Aware Graph2Seq Learning
Takeshi D. Itoh, Takatomi Kubo, Kazushi Ikeda
Graphs are a highly expressive data structure, but it is often difficult for humans to find patterns from a complex graph. Hence, generating human-interpretable sequences from grap…
Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities
Takeshi D. Itoh, Takatomi Kubo, Kazushi Ikeda
Graph neural networks (GNNs) have been widely used to learn vector representation of graph-structured data and achieved better task performance than conventional methods. The found…
Towards Generation of Visual Attention Map for Source Code
Takeshi D. Itoh, Takatomi Kubo, Kiyoka Ikeda +5
Program comprehension is a dominant process in software development and maintenance. Experts are considered to comprehend the source code efficiently by directing their gaze, or at…