JSAI2019

Presentation information

Organized Session

Organized Session » [OS] OS-4

[3D4-OS-4b] 自律・創発・汎用AIアーキテクチャ(2)

Thu. Jun 6, 2019 3:50 PM - 5:10 PM Room D (301B Medium meeting room)

栗原 聡(慶應義塾大学)、川村 秀憲(北海道大学)、津田 一郎(中部大学)、大倉 和博(広島大学)

4:10 PM - 4:30 PM

[3D4-OS-4b-02] Avoiding catastrophic forgetting in echo state networks by minimizing the connection cost

〇Yuji Kawai1, Yuho Ozasa1, Jihoon Park1, Minoru Asada1 (1. Osaka University)

Keywords:Echo state network, Continual learning, Catastrophic forgetting

Catastrophic forgetting is one of big issues in multi-task learning with neural networks. We propose that min-

imization of the connection cost mitigates catastrophic forgetting in echo state network. The optimization of

connections of reservoir network can yield neural modules (local sub-networks) that differentiate information

depending on tasks. The task-specic neural activities help to consolidate knowledges of the tasks. We showed

that this constraint creates neural modules consisting of negative connections and can improved the performance of

multi-task learning. Furthermore, we analyzed the transfer entropy of inter- and intra-modules to show task-specic

functional differentiation of the modules.