[Hiroki Matsutani] An Edge Learning Infrastructure Supporting Realtime and All-Data Capabilities

Research Director

ResearchDirector'sPhoto

Hiroki Matsutani

Keio University
Department of Information and Computer Science
Associate Professor

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Outline

A huge amount of stream data is generated continuously by industries and network services. For automated monitoring and anomaly detection by learning such big data, tendency changes of targets should be reflected to the learned parameters immediately, though deep learning is a time-consuming task. In this project, we will develop an AI infrastructure that can reflect recent data tendency in addition to all the past data to the learned parameters. We will validate our concept through experiments targeting industries and network services.

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  • CREST
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  • ERATO
  • ACT-X
  • ACCEL
  • ALCA
  • RISTEX
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