Deep Reinforcement Learning Workshop

The online-workshop comprises of a crash course and talk-sessions with invited speakers including panel discussions. The crash course aims to provide the participants with essential knowledge on deep reinforcement learning in order to understand the special features and advantages. It is designed as an interactive course. On the second day there will be talks and panel discussions with international speakers who work on highly relevant projects in that field.

Organized by: Viviane Clay & Ashima Keshava
YouTube live stream of the talks & panel discussions

Technisches Museum Wien is showing two of our explainer videos on AI

What is deep learning? Why do we need self-explaining neural networks? Two explainer videos that we originally made for the science exhibition ship "MS Wissenschaft" in 2019 will be presented beginning with December 2020 in the Technisches Museum Wien, Austria.

Higher, Further, and Smarter! – better encoding through interaction

New publication! How can agents develop an internal representation of the world with little or no rewards from the external environment – as it is in the case of humans? In her PhD project, Viviane Clay investigates how to teach a machine a semantic world understanding in a largely unsupervised fashion. For this she uses deep reinforcement learning testing the theory of embodiment. First results show the strength of embodied learning and its advantages over fully supervised approaches with regards to robustness and generalizability. Now published in Neural Networks.

Interview with Viviane Clay, winner of the Mendeley Data FAIRest Datasets Award

Open access datasets are absolutely essential for the researcher ecosystem for two reasons: „The first is for collaboration and moving the field forward together in a more efficient way. And the second is all about responsibility and accountability“ explains Viviane Clay who has won the Mendeley Data FAIRest Datasets Award as an important recognition for publishing data and analysis scripts in a reusable way. The whole interview is to be found on Elsevier Connect News.

Coronavirus forecasts: joint project with the Jülich Supercomputing Centre

Neuroinformatics scientists at Osnabrück University and data specialists at Forschungszentrum Jülich are releasing new model results daily to forecast COVID-19 infections. The results include estimates updated daily of the reported new infections and a 5-day forecast for every German district, and are available here. The predictions are based on data from the Robert Koch Institute, which are statistically analysed using a new model weighted by probability that was developed by Osnabrück's neuroinformatics scientists on high-performance computers at the Jülich Supercomputing Centre (JSC).

Lifehack for AI: Faster word learning similar to the kids

How can artificial agents  learn semantic representations faster, without
being told every word explicitly? Wouldn’t it be good if they were able to use
similar "lifehacks" as toddlers to learn new vocabulary? Our PHD student Xenia Ohmer developed a computational model of the „Mutual Exclusivity Bias“
(quickly rule out unlikely alternatives in order to effectively process and
learn word meanings) and by using reinforcement learning she trained
artificial agents to learn semantic representations faster.

Which linguistic cues improve discourse processing?

Linguistic cues are words that help to structure sentences to generate discourse expectations. There are many different types and less is known about the quality of their facilitative effects and whether the effects interact with one another. In a now published study our PHD student Juliane Schwab compared two types of cues (lexical and contextual discourse cues) in German and English for a cross-linguistic assessment of expectation-based effects in discourse processing. Understanding the role of such linguistic cues is essential to improve natural language communication between human and artificial agents.

Braving Covid-19: annual meeting of the German Research Training Groups in Computer Science

This year was our turn to organize the annual meeting of the German Research Training Groups which is traditionally hold in Dagstuhl. Due to Covid-19 we had to make it to an online event. Together with the Hasso Plattner Institute (Potsdam) we found an alternative online format that worked well for more than 50 participants.