Search Results for author: Hendrik Heuer

Found 7 papers, 1 papers with code

Writer-Defined AI Personas for On-Demand Feedback Generation

no code implementations19 Sep 2023 Karim Benharrak, Tim Zindulka, Florian Lehmann, Hendrik Heuer, Daniel Buschek

This is challenging, as writers may struggle to empathize with readers, get feedback in time, or gain access to the target group.

More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition

no code implementations5 Aug 2020 Hendrik Heuer, Andreas Breiter

This paper investigates the user experience of visualizations of a machine learning (ML) system that recognizes objects in images.

BIG-bench Machine Learning Object Recognition

How Fake News Affect Trust in the Output of a Machine Learning System for News Curation

no code implementations5 Aug 2020 Hendrik Heuer, Andreas Breiter

In a study with 82 vocational school students with a background in IT, we found that users are able to provide trust ratings that distinguish trustworthy recommendations of quality news stories from untrustworthy recommendations.

BIG-bench Machine Learning

Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation

1 code implementation WS 2019 Maximilian Spliethöver, Jonas Klaff, Hendrik Heuer

Attention mechanisms have seen some success for natural language processing downstream tasks in recent years and generated new State-of-the-Art results.

Sentence Word Embeddings

Generating captions without looking beyond objects

no code implementations12 Oct 2016 Hendrik Heuer, Christof Monz, Arnold W. M. Smeulders

This paper explores new evaluation perspectives for image captioning and introduces a noun translation task that achieves comparative image caption generation performance by translating from a set of nouns to captions.

Caption Generation Image Captioning +2

Text comparison using word vector representations and dimensionality reduction

no code implementations2 Jul 2016 Hendrik Heuer

This paper describes a technique to compare large text sources using word vector representations (word2vec) and dimensionality reduction (t-SNE) and how it can be implemented using Python.

Dimensionality Reduction

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