Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi. "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference", AAMAS, 2021.
のスライド
Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi. "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference", AAMAS, 2021.
のスライド
This slide is my presentation for a reading circle "Machine Learning Professional Series".
Japanese version is here.
http://www.slideshare.net/matsukenbook/ss-50545587
Introduction of Chainer, a framework for neural networks, v1.11. Slides used for the student seminar on July 20, 2016, at Sugiyama-Sato lab in the Univ. of Tokyo.
This slide is my presentation for a reading circle "Machine Learning Professional Series".
Japanese version is here.
http://www.slideshare.net/matsukenbook/ss-50545587
Introduction of Chainer, a framework for neural networks, v1.11. Slides used for the student seminar on July 20, 2016, at Sugiyama-Sato lab in the Univ. of Tokyo.
리츠메이칸대학 정보이공학부를 소개하는 한글 자료입니다. 리츠메이칸대학 정보이공학부는 약100명의 교수진과 약 50개 연구실로 구성된 일본 최대급의 컴퓨터 공학 분야를 배우는 학부입니다. 2017년 부터 새롭게 개편된 제도로 신입생을 선발하는데 특징은 7개의 코스 제도이며 그 중 한 코스인 정보 시스템 글로벌 코스는 입시 부터 교육 과정 까지 모두 영어로 행하여지는 코스입니다. 컴퓨터 공학, 로봇 공학 등에 관심 있으신 고등학생들을 위하여 만든 설명 자료이오니 많이 봐주시면 감사하겠습니다. 참고로 7개 코스 중 나머지 6개 코스는 일본어로 교육을 하기 때문에 입시도 일본어 능력이 필요합니다. 따라서 본 자료 내용중 6개 코스에 대해서는 일부 일본어로 기술되어 있음을 알려 드립니다.
DeepRemote: A Smart Remote Controller for Intuitive Control through Home Appl...Yuta Takahashi
This document describes DeepRemote, a smart remote controller that uses deep learning for intuitive home appliance selection and control. It consists of a control unit with a camera and buttons and a deep learning unit for appliance recognition. The system was tested for classification accuracy of over 80% on average, response time of under 2 seconds, and faster control times than traditional remotes in user tests. Overall, DeepRemote demonstrates an effective deep learning approach for selecting and controlling home appliances intuitively with a single remote controller.
An Identification Method of IR Signals to Collect Control Logs of Home Applia...Yuta Takahashi
This document proposes a method to identify infrared (IR) signals from home appliances in order to collect control logs. It involves preprocessing raw IR signals into pulse width sequences, comparing signals using mean absolute error and sum absolute error, and constructing statistical models to identify appliance type with 95.5% accuracy and command type with 92% accuracy based on a database of 1,400 signals from 14 appliances. A simple simulation shows identification stability is achieved when the database includes 6 or more signals per appliance. The method could help automatically understand user preferences from appliance usage logs.