Last time, I gave a rough outline of a hybrid approach for obtaining the dimensions of a conceptual space that uses both multidimensional scaling (MDS) and artificial neural networks (ANNs) [1]. Today, I will show our first results (which we will present next week at the AIC workshop in Palermo).
Continue reading “A hybrid way for obtaining the dimensions of a conceptual space (Part 2)”
In earlier blog posts, I have already talked about two ways of obtaining the dimensions of a conceptual space: Neural networks such as InfoGAN on the one hand and multidimensional scaling (MDS) on the other hand. Over the past few months, in a collaboration with Elektra Kypridemou, I have worked on a way of combining these two approaches. Today, I would like to give a quick overview of our recent proposal [1].
Continue reading “A hybrid way for obtaining the dimensions of a conceptual space (Part 1)”
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.
A while ago, I introduced Logic Tensor Networks (LTNs) and argued that they are nicely applicable in a conceptual spaces scenario. In one of my recent posts, I described how to ensure that an LTN can only learn convex concepts. Today, I will take this one step further by introducing additional ways of defining the membership function of an LTN.
I’ve recently shared my first (and unfortunately relatively disappointing) results of applying InfoGAN [1] to simple shapes. Over the past weeks, I’ve continued to work on this, and my results are starting to look more promising. Today, I’m going to share the current state of my research.
