Part 1. What It Is and Why We Do It

In this posting, I’ll introduce an interesting method that we often use in our research, called the Error Regression Scheme (ERS) [1-2]. In short, the ERS is a sort of online optimization technique, but it is different from other techniques in several ways. For instance, during the ERS, the weights are not updated. Instead, the neuron’s actual values are updated to minimize the error at the output. The ERS is a kind of prediction error minimization mechanism and there are several (philosophical) thoughts behind it. I’ll talk about them later in other postings. Let’s begin with what it is and why we use it.

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2017년 11월 19일. OIST에서 Science Festival가 열렸다. 일종의 Open Campus같은 것인데, KAIST에서 하던 Open Campus랑은 많이 달랐다. (OIST가 KAIST보다 훨씬 규모가 작아서 그런지) 행사 준비를 하고 진행을 하면서, 학교측에서 행사 관리/운영을 되게 잘하는구나 하고 느꼈다. 그리고 OIST는 이런 류의 행사 - 학교라는 울타리 안에서 진행되던 연구를 울타리 밖의 사람들에게 소개하는 기회 - 를 소중히 여기는 듯 하였다. 그 점은 나도 극히 동감.

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One of the difficulties that I faced when I joined Cognitive Neurorobotics lab was that I wasn’t familiar with the terms used in the lab. Some terms are from the field of dynamics and some other terms were “coined” by my advisor (Prof. Tani). So, it took me quite a time to understand them. I guess it might be a bit more difficult for other people sometimes.

So, I’d like to briefly explain those terms that can be frequently found in my studies on “cognitive neurorobotics” or in Tani’s book (“Exploring Robotic Minds Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena”). This post is targeted to the general audiences (someone like me five years ago). So those terms won’t be explained in a great detail. Instead, I’ll just try to give a general idea about them.

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2017년 11월 28일. Pfeifer 교수님이 OIST를 방문했다. 아는 사람은 알겠지만.. 맞다. 바로 그 유명한 책 “How the Body Shapes the Way We Think”의 저자! 무려 자신의 Wikipedia 페이지가 있는 인물이다! Respect!

Kenji Doya 교수님의 연구실을 방문하고 세미나를 한건데, 좋은 기회를 놓치지 않고 참여할 수 있었다. 그리고 세미나 후엔 내 연구를 간략히 소개해드리는 기회도 얻었다. 시간에 쫓겨서 자세히 소개는 못해드렸지만, 그래도 embodied cognition approach로 연구를 소개하니 잘 들어주신듯 하다.

아래는 Pfeifer 교수님의 세미나 중 메모한 내용을 정리..그냥 올린 것이다. 전반적으로 Soft Robotics의 중요성을 설파하고 가셨다. Soft Robotics라는 저널도 소개하고 가셨는데, 오늘 (2018년 1월 10일) 확인해보니, Impact Factor가 무려 8.649!!!! 로보틱스에서 이런 Impact Factor는 처음 본다.. 교수님의 세미나 내용 (키워드 위주)과 중간중간에 든 나의 의문점들을 나중에 까먹지 않기 위해서 일단 올려 본다.

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When I train my neural network models, I often use the method called “Softmax Transformation”. It is the method of representing the training data into the sparse form. When I first learned how to do it, I had some troubles ‘cause there wasn’t enough example about how to do it. And still, I can’t find nice explanation about the softmax transformation with examples. So here I have some brief explanation and sample codes for the softmax transformation. Let’s see how the softmax transformation works step by step.

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Last September, I attended the ICDL-EPIROB 2017 which was held in Lisbon, Portugal. I really enjoyed the conference and the beautiful city and I also had a great time with my old & new friends :)

This time, I was lucky to have an opportunity for the oral presentation. I had my presentation on my recent study on visuomotor learning, particularly on P-VMDNN (Predictive Visuo-Motor Deep Dynamic Neural Network).

The manuscript, the presentation slides (pdf) and the supplementary videos that I used during my talk can be downloaded from below:

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Jungsik Hwang

황중식, 물결, mulkkyul, PhD Student@KAIST, SRS@OIST