25 citations · 179 across the 54 of their papers we have counts for
12 papers · 1 filter
LossVal: Efficient Data Valuation for Neural Networks
Tim Wibiral, Mohamed Karim Belaid, Maximilian Rabus +1
Assessing the importance of individual training samples is a key challenge in machine learning. Traditional approaches retrain models with and without specific samples, which is co…
Edge-Splitting MLP: Node Classification on Homophilic and Heterophilic Graphs without Message Passing
Matthias Kohn, Marcel Hoffmann, Ansgar Scherp
Message Passing Neural Networks (MPNNs) have demonstrated remarkable success in node classification on homophilic graphs. It has been shown that they do not solely rely on homophil…
Semantic Web: Past, Present, and Future (with Machine Learning on Knowledge Graphs and Language Models on Knowledge Graphs)
Ansgar Scherp, Gerd Groener, Petr Škoda +2
Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on th…
Efficient Continual Learning for Small Language Models with a Discrete Key-Value Bottleneck
Andor Diera, Lukas Galke, Fabian Karl +1
Continual learning remains a challenge across various natural language processing (NLP) tasks, as models updated with new training data often risk catastrophic forgetting of previo…
Isotropy Matters: Soft-ZCA Whitening of Embeddings for Semantic Code Search
Andor Diera, Lukas Galke, Ansgar Scherp
Low isotropy in an embedding space impairs performance on tasks involving semantic inference. Our study investigates the impact of isotropy on semantic code search performance and…
F -- A Model of Events based on the Foundational Ontology DOLCE+DnS Ultralite
Ansgar Scherp, Thomas Franz, Carsten Saathoff +1
The lack of a formal model of events hinders interoperability in distributed event-based systems. In this paper, we present a formal model of events, called Event-Model-F. The mode…
Your Extreme Multi-label Classifier is Secretly a Hierarchical Text Classifier for Free
Nerijus Bertalis, Paul Granse, Ferhat Gül +6
Assigning a set of labels to a given text is a classification problem with many real-world applications, such as recommender systems. Two separate research streams address this iss…
HyperAggregation: Aggregating over Graph Edges with Hypernetworks
Nicolas Lell, Ansgar Scherp
HyperAggregation is a hypernetwork-based aggregation function for Graph Neural Networks. It uses a hypernetwork to dynamically generate weights in the size of the current neighborh…
Lifelong Graph Learning for Graph Summarization
Jonatan Frank, Marcel Hoffmann, Nicolas Lell +2
Summarizing web graphs is challenging due to the heterogeneity of the modeled information and its changes over time. We investigate the use of neural networks for lifelong graph su…
Multi-View Structural Graph Summaries
Jonatan Frank, Andor Diera, David Richerby +1
A structural graph summary is a small graph representation that preserves structural information necessary for a given task. The summary is used instead of the original graph to co…
POWN: Prototypical Open-World Node Classification
Marcel Hoffmann, Lukas Galke, Ansgar Scherp
We consider the problem of \textit{true} open-world semi-supervised node classification, in which nodes in a graph either belong to known or new classes, with the latter not presen…
Text Role Classification in Scientific Charts Using Multimodal Transformers
Hye Jin Kim, Nicolas Lell, Ansgar Scherp
Text role classification involves classifying the semantic role of textual elements within scientific charts. For this task, we propose to finetune two pretrained multimodal docume…