In a previous mini-series of blog posts (see here, here, here, and here), I have introduced a small data set of 60 line drawings complemented with pairwise shape similarity ratings and analyzed this data set in the form of conceptual similarity spaces. Today, I will start a new mini-series about learning a mapping from images into these similarity spaces, following up on my prior work on the NOUN dataset (see here and here).
Since this is going to be my last blog post for this year, I’m going to use it for reflecting a bit on my academic life this year and for talking a bit about my plans for 2021.
Since I’m still working on background chapters for my dissertation and hence currently do not have to share any research updates, I’m going to focus today’s blog post on my teaching duties. More specifically, I’ll try to convince you that grading sheets are a useful tool for making your own grading process more structured and objective, as well as for providing students with valuable feedback. Continue reading “What are “grading sheets” and why do we need them?”
It’s about time for another blog post in my little “What is …?” series. Today I want to talk about a specific type of artificial neural network, namely convolutional neural networks (CNNs). CNNs are the predominant approach for classifying images and have already been implicitly used in my study on the NOUN data set as well as in the analysis of the Shape similarity ratings. With this blog post, I want to clarify the basic underlying structure of this type of neural networks.
I’m currently writing the background chapters for my dissertation, so there’s not much news from the research front. However, I’ve had the pleasure to organize and participate in several virtual events over the past weeks. Today, I therefore want to share some thoughts on “virtual academia”.