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”.
This post will be the last one in my mini-series “A Similarity Space for Shapes” about joint work with Margit Scheibel. So far, I have described the overall data set, the correlation between distances and dissimilarities, and the well-shapedness of conceptual regions. Today, I will finally take a look at interpretable directions in this similarity space.
Today, I would like to continue my little series about recent joint work with Margit Scheibel on a psychologically grounded similarity space for shapes. In my first post, I outlined the data set we worked with, and in my second post, we investigated how well the dissimilarity ratings are reflected by distances in the similarity spaces. Today, I’m going to use the categories from our data set to analyze whether conceptual regions are well-formed.