2 citations · 7 across the 9 of their papers we have counts for
9 papers
iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
Nicolas Lell, Ansgar Scherp
Shallow node embeddings like node2vec (N2V) can be used for nodes without features or to supplement existing features with structure-based information. Embedding methods like N2V a…
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…
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…
Fine-Tuning Language Models for Scientific Writing Support
Justin Mücke, Daria Waldow, Luise Metzger +4
We support scientific writers in determining whether a written sentence is scientific, to which section it belongs, and suggest paraphrasings to improve the sentence. Firstly, we p…
The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
Nicolas Lell, Ansgar Scherp
When training a Neural Network, it is optimized using the available training data with the hope that it generalizes well to new or unseen testing data. At the same absolute value,…
Memorization of Named Entities in Fine-tuned BERT Models
Andor Diera, Nicolas Lell, Aygul Garifullina +1
Privacy preserving deep learning is an emerging field in machine learning that aims to mitigate the privacy risks in the use of deep neural networks. One such risk is training data…
Reducing a Set of Regular Expressions and Analyzing Differences of Domain-specific Statistic Reporting
Tobias Kalmbach, Marcel Hoffmann, Nicolas Lell +1
Due to the large amount of daily scientific publications, it is impossible to manually review each one. Therefore, an automatic extraction of key information is desirable. In this…
STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topics from Scientific Papers
Steffen Epp, Marcel Hoffmann, Nicolas Lell +2
A common writing style for statistical results are the recommendations of the American Psychology Association, known as APA-style. However, in practice, writing styles vary as repo…