Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative

no code implementations • 13 Jul 2023 • Sho Shimoyama, Tetsuro Morimura, Kenshi Abe, Toda Takamichi, Yuta Tomomatsu, Masakazu Sugiyama, Asahi Hentona, Yuuki Azuma, Hirotaka Ninomiya

One way to estimate rewards from collected data is to train the reward estimator and dialog policy simultaneously using adversarial learning (AL).

Reinforcement Learning (RL)

Community Detection and Growth Potential Prediction from Patent Citation Networks

no code implementations • 23 Apr 2019 • Asahi Hentona, Takeshi Sakumoto, Hugo Alberto Mendoza España, Hirofumi Nonaka, Shotaro Kataoka, Toru Hiraoka, Kensei Nakai, Elisa Claire Alemán Carreón, Masaharu Hirota

The scoring of patents is useful for technology management analysis.

Clustering Community Detection +3

Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks

no code implementations • 23 Apr 2019 • Kensei Nakai, Hirofumi Nonaka, Asahi Hentona, Yuki Kanai, Takeshi Sakumoto, Shotaro Kataoka, Elisa Claire Alemán Carreón, Toru Hiraoka

Scoring patent documents is very useful for technology management.

Clustering Community Detection +3

Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models

no code implementations • 15 Apr 2019 • Elisa Claire Alemán Carreón, Hirofumi Nonaka, Asahi Hentona, Hirochika Yamashiro

In response to this, we applied machine learning algorithms SVM and XGBoost, as well as Logistic Regression, to construct a number of prediction models based on at-home advertisement exposure time and demographic data, examining the predictability of Actual Purchase and Purchase Intention behaviors of 3000 customers across 36 different products during the span of 3 months.