Some time ago, I wrote two blog posts about a hybrid way for obtaining the dimensions of a conceptual space (see here and here). Currently, I’m rerunning these experiments in a more detailed way and today I want to share both the motivation for doing this as well as some first results.

# Category: Machine learning

## Applying Logic Tensor Networks (Part 5)

Last time, I have shared the first results obtained by the LTN on the conceptual space of movies. Today, I want to give you a quick update on the first membership function variant that I have investigated.

## Applying Logic Tensor Networks (Part 4)

After having already written a lot about Logic Tensor Networks, today I will finally share some first results of how they perform in a multi-label classification task on the conceptual space of movies.

## What is a “β Variational Autoencoder”?

I’ve already talked about InfoGAN [1] 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) [2] which uses a different approach for reaching the same goal.

## Applying Logic Tensor Networks (Part 3)

Last time, I have introduced the evaluation metrics used for the LTN classification task. Today, I will show some first results of the *k* nearest neighbor (*k*NN) classifier which will serve as a baseline for our LTN results.