activity
20182021
most citedConversational Agents for Insurance Companies: From Theory to Practice

13 citations · 27 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2021

Evaluation of Representation Models for Text Classification with AutoML Tools

Sebastian Brändle, Marc Hanussek, Matthias Blohm +1

Automated Machine Learning (AutoML) has gained increasing success on tabular data in recent years. However, processing unstructured data like text is a challenge and not widely sup…

cs.LG2020

Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance

Matthias Blohm, Marc Hanussek, Maximilien Kintz

Recently, Automated Machine Learning (AutoML) has registered increasing success with respect to tabular data. However, the question arises whether AutoML can also be applied effect…

cs.LG2020

Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark

Marc Hanussek, Matthias Blohm, Maximilien Kintz

In the last few years, Automated Machine Learning (AutoML) has gained much attention. With that said, the question arises whether AutoML can outperform results achieved by human da…

cs.HC2019★ 13 cited

Conversational Agents for Insurance Companies: From Theory to Practice

Falko Koetter, Matthias Blohm, Jens Drawehn +4

Advances in artificial intelligence have renewed interest in conversational agents. Additionally to software developers, today all kinds of employees show interest in new technolog…

cs.HC2018★ 12 cited

Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies

Falko Koetter, Matthias Blohm, Monika Kochanowski +3

Advances in artificial intelligence have renewed interest in conversational agents. So-called chatbots have reached maturity for industrial applications. German insurance companies…

cs.CL2018★ 2 cited

Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension

Matthias Blohm, Glorianna Jagfeld, Ekta Sood +2

We propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question a…