I’ve recently shared my first (and unfortunately relatively disappointing) results of applying InfoGAN  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.
I have already talked about Logic Tensor Networks (LTNs for short) in the past (see here and here) and I’ve announced to work with them. Today, I will share with you my first steps with respect to modifying and extending the framework. More specifically, I will talk about a problem with the original membership function and about how I solved it.
A while back, I talked about using InfoGAN networks to learn interpretable dimensions for the shape domain of a conceptual space. As this has already been a few months ago, I think it is now time for an update. Where do I stand with my research with respect to this topic?
In my last blog post, I have introduced the general idea of Logic Tensor Networks (or LTNs, for short). Today I would like to talk about how LTNs and conceptual spaces can potentially fit together and about the concrete strands of research I plan to pursue.
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.