The CoRisk-Index extends the spectre of existing, but less frequently updated economic indicators by tapping into an unexploited and highly abundant data source. The new indicator can be considered business-internally since corporations provide information themselves and data fidelity is self-incentivized due to law enforcement.

Fabian Stephany 5
Niklas Stoehr 9
Philipp Darius 10
Leonie Neuhäuser 10
Ole Teutloff 10
Fabian Braesemann 5

CEPR Covid Economics

↗ arXiv Paper
↗ CEPR Paper
↗ Online Dashboard
↗ Explanation Video
↗ OII Blog Post
↗ Oxford Blog Post
↗ Wirtschaftswoche
↗ Washington Post
↗ Talk at UNDP
↗ Talk at ODID
↗ BibTeX

This work approaches graph generation (decoding) as the inverse of graph compression (encoding). We show that in a disentanglement-focused deep autoencoding framework, specifically Beta-Variational Autoencoders (Beta-VAE), choices of generative procedures and their parameters arise naturally in the latent space. 

Niklas Stoehr 3
Emine Yilmaz 3
Marc Brockschmidt 6
Jan Stuehmer 6

Workshop on Graph Representation Learning, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver

↗ arXiv Paper
↗ NeurIPS Paper
↗ Presentation Video
↗ Interactive Code
↗ Poster
↗ Slides
↗ BibTeX

The proximity between newspapers and political parties is strongly subjective and difficult to measure. Yet, political tendencies of newspapers can have a significant impact on voters’ opinion‐forming and ought to be known by the public in a transparent and timely manner. This article introduces the Sentiment Political Compass (SPC), a data‐driven framework for analyzing political bias of newspapers toward political parties.

Fabian Falck 4
Julian Marstaller 7
Niklas Stoehr 5
Sören Maucher 7
Jeana Ren 7
Andreas Thalhammer 7
Achim Rettinger 7
Rudi Studer 7

Policy & Internet, Wiley Online Library, 2019

↗ Pre-Print Paper
↗ Journal Paper
↗ Code
↗ Data
↗ Interactive Website
↗ Speakerpolitics
↗ BibTeX

The digital transformation is driving revolutionary innovations and new market entrants threaten established sectors of the economy such as the automotive industry. Following the need for monitoring shifting industries, we present a network-centered analysis of car manufacturer web pages. Solely exploiting publicly-available information, we construct large networks from web pages and hyperlinks. We tag web pages concerned with topics like e-mobility and environment or autonomous driving, and investigate their relevance in the network. 

Niklas Stoehr 9
Fabian Braesemann 5
Michael Frommelt 9
Shi Zhou 3

CompleNet 2020, published in Springer Nature

↗ arXiv Paper
↗ Springer Paper
↗ IBM Blog Post
↗ IBM Livestudio
↗ OII Blog Post
↗ Presentation Video
↗ Code
↗ BibTeX

To understand the dynamics of the digital knowledge economy, it is crucial to reveal the geography of global flows of knowledge on digital platforms. This article visualizes a key form of knowledge production in the digital economy: mapping the joint collaborations of users from different cities on Stack Overflow, the world’s most popular question-and-answer website for programming questions.

Fabian Braesemann 5
Niklas Stoehr 3
Marc Graham 5

Regional Studies, Regional Science, Volume 6, 2019

↗ Journal Paper
↗ OII Blog Post
↗ Code
↗ BibTeX

We present a novel middleware technique called Heatflip, which issues diametrically opposed samples into the temporal and spatial dimensions of the data stored in an external database. Spatial samples provide insights into the temporal distribution and vice versa.

Niklas Stoehr 1
Johannes Meyer 1
Volker Markl 1
Qiushi Bai 2
Taewoo Kim 2
De-Yu Chen 2
Chen Li 2

2018 IEEE International Conference on Big Data (Big Data)

↗ Open Paper
↗ IEEE Paper
↗ BibTeX

In this work, we thoroughly analyse the ability of text classification models to adapt to transfer learning tasks, whether they are specifically designed for it or not. We directly compare the Transformer Model with an attention-based bi-directional LSTM and naive Logistic Regression as a baseline.

Niklas Stoehr 3
Luis Sanchez 3
Daniil Gannota 3
Fabian Falck 8

2019 IEEE Social Network Management and Security (SNAMS), Second Workshop on Deep and Transfer Learning

↗ IEEE Paper
↗ Code
↗ Presentation Video
↗ BibTeX

In this work, we extend Siamese neural networks to few-shot and zero-shot learning. For few-shot learning, we augment the conventional dual encoder architecture to contain (k+1) encoder branches. For zero-shot learning, we propose Semantic Asymmetric Siamese Networks (SemASN), an asymmetric architecture with two distinct, non-weight sharing encoders on different data modalities (here images and semantic descriptions).

Niklas Stoehr 5   
Fabian Falck 5
Jiaoyan Chen 5
Bernardo Cuenca Grau 5

work in progress

work in progress

Fabian Stephany 5
Niklas Stoehr 9
Philipp Darius 10
Leonie Neuhäuser 10
Ole Teutloff 10
Fabian Braesemann 5

↗ arXiv Paper
↗ CEPR Paper
↗ Online Dashboard
↗ Explanation Video
↗ OII Blog Post
↗ Oxford Blog Post
↗ Wirtschaftswoche
↗ Washington Post
↗ Talk at UNDP
↗ Talk at ODID
↗ BibTeX

Niklas Stoehr 3
Emine Yilmaz 3
Marc Brockschmidt 6
Jan Stuehmer 6

↗ arXiv Paper
↗ NeurIPS Paper
↗ Presentation Video
↗ Interactive Code
↗ Poster
↗ Slides
↗ BibTeX

Fabian Falck 4
Julian Marstaller 7
Niklas Stoehr 5
Sören Maucher 7
Jeana Ren 7
Andreas Thalhammer 7
Achim Rettinger 7
Rudi Studer 7

↗ Pre-Print Paper
↗ Journal Paper
↗ Code
↗ Data
↗ Interactive Website
↗ Speakerpolitics
↗ BibTeX

Niklas Stoehr 9
Fabian Braesemann 5
Michael Frommelt 9
Shi Zhou 3

↗ arXiv Paper
↗ Springer Paper
↗ IBM Blog Post
↗ IBM Livestudio
↗ OII Blog Post
↗ Presentation Video
↗ Code
↗ BibTeX

Fabian Braesemann 5
Niklas Stoehr 3
Marc Graham 5

↗ Journal Paper
↗ OII Blog Post
↗ Code
↗ BibTeX

Niklas Stoehr 1
Johannes Meyer 1
Volker Markl 1
Qiushi Bai 2
Taewoo Kim 2
De-Yu Chen 2
Chen Li 2

↗ Open Paper
↗ IEEE Paper
↗ BibTeX

Niklas Stoehr 3
Luis Sanchez 3
Daniil Gannota 3
Fabian Falck 8

↗ IEEE Paper
↗ Code
↗ Presentation Video
↗ BibTeX

Niklas Stoehr 5   
Fabian Falck 5
Jiaoyan Chen 5
Bernardo Cuenca Grau 5

work in progress