Satoshi Tsutsui

TL;DR: Examination of how research topics evolve by analyzing the topic trends, evolving dynamics, and semantic word shifts in the IR domain shows that the evolution of a major topic usually follows a pattern from adjusting status to mature status, and sometimes with re-adjusting status in between the evolving process.

TL;DR: The edge2vec model, which represents graphs considering edge semantics, is proposed, which is validated on three biomedical domain tasks and significantly outperforms state-of-the-art models on all three tasks.

TL;DR: A data driven approach to separate compound figures using modern deep Convolutional Neural Networks to train the separator in an end-to-end manner is proposed, using transfer learning as well as automatically synthesizing training exemplars.

TL;DR: A meta-learning framework to reinforce the generated images by original images so that these images can facilitate one-shot learning and a Meta Image Reinforcing Network (MetaIRNet) is proposed to conduct one- shot fine-grained recognition as well as image reinforcement.

TL;DR: In this article, the edge2vec model is proposed to represent graphs considering edge semantics, and an edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix.