In this blogpost, I want to present my master's thesis, which focused on transfer learning for text classification using Siamese Networks.

Text classification is a field in natural language processing (NLP), which assigns text to given classes. With applications in sentiment analysis, spam detection or i

Teach your Deep Neural Network to be aware of its epistemic and aleatory uncertainty. Get a quantified confidence measure for your Deep Learning predictions.

Artificial Intelligence—and machine learning in particular—have come a long way since their early beginnings. The widespread availability and affordability of powerfu

Sequential recommender systems are based on sequential user representations for a given user and sequence length. Each sequence consists of several items in temporal order. Sequential recommender systems aim at exploiting the temporal information that is hidden in the sequence of item interactions of the given user.

Since the invention of the internet, the availability and amount of information has increased steadily. Today we are facing problems of information overload and an ov

In this article I describe the steps and approaches to image recognition for receipt digitalization using computer vision. This is the basic functionality behind apps such as Google Lens, Evernote, PaperScan and taggun.io.

“Would you like the receipt?”—It’s hard to say no to that. Not because you actually want it (you may even throw it in the trash before exiting the store), but because

We extend state-of-the-art  sequence-to-sequence neural networks for summarization of long text across windows. By learning transitions, we are able to process arbitrarily long texts during inference.

This blog post describes my master thesis "Abstractive Summarization for Long Texts". We’ve extended existing state-of-the-art  sequence-to-sequence (Seq2Seq) neural net

The idea behind the model-agnostic technique LIME is to approximate a complex model locally by an interpretable model and to use that simple model to explain a prediction of a particular instance of interest.

This is the second part of our series about Machine Learning interpretability. We want to describe LIME (Local Interpretable Model-Agnostic Explanations), a popular t

This blog explains the basic concept of Reinforcement Learning, giving you an understanding of the closed loop system, in which an agent uses actions to change the state of the environment and thus receives rewards, with the goal of maximizing the return.

I would like to start this series about reinforcement learning by giving an overview of what reinforcement learning is, what it is used for and what terminology is ne

Entropy is a significant, widely used and above all successful measure for quantifying eg. inhomogeneity, uncertainty or unpredictability. It is an integral part of the latest machine learning models deployed on real-world data sets. In this article, I want to highlight the simplicity, beauty and meaning of entropy.

If you are dealing with Statistics, Data Science, Machine Learning, Artificial Intelligence or even general Computer Science, Mathematics, Engineering or Physics, you

Everybody talks about AI and deep learning and everybody uses it, including you! But what exactly is deep learning and what are artificial neural networks? In this article I shine a light on some basic yet crucial concepts in an attempt to lift the veil.

Artificial intelligence or deep learning: Everybody talks about it and everybody uses it, including you! Of course you immediately have the evil terminator in mind wh

I use state-of-the-art NLP techniques to improve an existing pricing model in an online car market. Online car markets usually use technical car attributes for price prediction with sellers adding description texts to provide more details. In my thesis, I use these texts to improve the existing pricing model.

tl;dr: This blog post summarizes my masters‘ thesis. I use state-of-the-art NLP techniques to improve an existing pricing model in an online car market. Online