In the past, we have already talked about some machine learning models, including LTNs and β-VAE. Today, I would like to introduce the basic idea of linear support vector machines (SVMs) and how they can be useful for analyzing a conceptual space. Continue reading “What is a “Support Vector Machine”?”
I’ve already talked about InfoGAN  a couple of times (here, here, and here). InfoGAN is a specific neural network architecture that claims to extract interpretable and semantically meaningful dimensions from unlabeled data sets – exactly what we need in order to automatically extract a conceptual space from data.
InfoGAN is however not the only architecture that makes this claim. Today, I will talk about the β-variational autoencoder (β-VAE)  which uses a different approach for reaching the same goal.
I’ve already talked about how to potentially obtain the dimensions of a conceptual space with artificial neural networks in a previous blog post. That approach is based on machine learning techniques, but there’s also a more traditional way of extracting a conceptual space: Conducting a psychological experiment and using a type of algorithm called “multidimensional scaling”. Today, I would like to give a quick overview of this approach.
Over the past few weeks, I have been pretty busy fulfilling my teaching duties. As I haven’t done much researching, I won’t talk about research today, but about “Constructive Alignment”, which is an approach for planning lectures, seminars and other courses.
The constructive alignment process consists of three steps:
- Defining the learning targets
- Planning the examination
- Planning the course
But wait a second, why does planning the course appear as the last step in this process?
About half a year ago, I mentioned “Logic Tensor Networks” in my short summary of the Dagstuhl seminar on neural-symbolic computation. I think that this is a highly interesting approach, and as I intend to work with it in the future, I will shortly introduce this framework today.