207 citations · 253 across the 25 of their papers we have counts for
7 papers · 1 filter
An Overview of Computational Approaches for Interpretation Analysis
Philipp Blandfort, Jörn Hees, Desmond U. Patton
It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an i…
The Focus-Aspect-Polarity Model for Predicting Subjective Noun Attributes in Images
Tushar Karayil, Philipp Blandfort, Jörn Hees +1
Subjective visual interpretation is a challenging yet important topic in computer vision. Many approaches reduce this problem to the prediction of adjective- or attribute-labels fr…
Towards Semantically Enhanced Data Understanding
Markus Schröder, Christian Jilek, Jörn Hees +1
In the field of machine learning, data understanding is the practice of getting initial insights in unknown datasets. Such knowledge-intensive tasks require a lot of documentation,…
Simplified SPARQL REST API - CRUD on JSON Object Graphs via URI Paths
Markus Schröder, Jörn Hees, Ansgar Bernardi +3
Within the Semantic Web community, SPARQL is one of the predominant languages to query and update RDF knowledge. However, the complexity of SPARQL, the underlying graph structure a…
An Easy & Collaborative RDF Data Entry Method using the Spreadsheet Metaphor
Markus Schröder, Christian Jilek, Jörn Hees +2
Spreadsheets are widely used by knowledge workers, especially in the industrial sector. Their methodology enables a well understood, easy and fast possibility to enter data. As fil…
What do Deep Networks Like to See?
Sebastian Palacio, Joachim Folz, Jörn Hees +3
We propose a novel way to measure and understand convolutional neural networks by quantifying the amount of input signal they let in. To do this, an autoencoder (AE) was fine-tuned…
Adversarial Defense based on Structure-to-Signal Autoencoders
Joachim Folz, Sebastian Palacio, Joern Hees +2
Adversarial attack methods have demonstrated the fragility of deep neural networks. Their imperceptible perturbations are frequently able fool classifiers into potentially dangerou…