25 citations · 179 across the 54 of their papers we have counts for
9 papers · 1 filter
Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes
Lukas Galke, Iacopo Vagliano, Benedikt Franke +3
Lifelong graph learning deals with the problem of continually adapting graph neural network (GNN) models to changes in evolving graphs. We address two critical challenges of lifelo…
Time and Memory Efficient Parallel Algorithm for Structural Graph Summaries and two Extensions to Incremental Summarization and -Bisimulation for Long -Chaining
Till Blume, Jannik Rau, David Richerby +1
We developed a flexible parallel algorithm for graph summarization based on vertex-centric programming and parameterized message passing. The base algorithm supports infinitely man…
General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings
Lukas Galke, Isabelle Cuber, Christoph Meyer +3
Large pretrained language models (PreLMs) are revolutionizing natural language processing across all benchmarks. However, their sheer size is prohibitive for small laboratories or…
Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP
Lukas Galke, Ansgar Scherp
Graph neural networks have triggered a resurgence of graph-based text classification methods, defining today's state of the art. We show that a wide multi-layer perceptron (MLP) us…
Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input Modalities
Iacopo Vagliano, Lukas Galke, Ansgar Scherp
We address the problem of recommending relevant items to a user in order to "complete" a partial set of items already known. We consider the two scenarios of citation and subject l…
Analysis of GraphSum's Attention Weights to Improve the Explainability of Multi-Document Summarization
M. Lautaro Hickmann, Fabian Wurzberger, Megi Hoxhalli +3
Modern multi-document summarization (MDS) methods are based on transformer architectures. They generate state of the art summaries, but lack explainability. We focus on graph-based…
rx-anon -- A Novel Approach on the De-Identification of Heterogeneous Data based on a Modified Mondrian Algorithm
Fabian Singhofer, Aygul Garifullina, Mathias Kern +1
Traditional approaches for data anonymization consider relational data and textual data independently. We propose rx-anon, an anonymization approach for heterogeneous semi-structur…
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…
A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes
Ishwar Venugopal, Jessica Töllich, Michael Fairbank +1
Deep-learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have been successfully used for process-mining tasks. They have achieved better p…