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.

# Category: Machine learning

## 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.

## Applying Logic Tensor Networks (Part 2)

In my last LTN blog post, I introduced the overall setting of my experiment. Before I can report on first results, I want and need to describe how we can evaluate the performance of the classifiers in this multi-label classification setting. This is what I’m going to do today.

## Applying Logic Tensor Networks (Part 1)

In previous blog posts I have already talked about Logic Tensor Networks in general, their relation to Conceptual Spaces, and several additional membership functions that are in line with the Conceptual Spaces framework. As I already mentioned before, I want to apply them in a “proof of concept” scenario. Today I’m going to sketch this scenario in more detail.