6 citations · 23 across the 19 of their papers we have counts for
7 papers · 1 filter
Knowledge Graph Structure as Prompt: Improving Small Language Models Capabilities for Knowledge-based Causal Discovery
Yuni Susanti, Michael Färber
Causal discovery aims to estimate causal structures among variables based on observational data. Large Language Models (LLMs) offer a fresh perspective to tackle the causal discove…
AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning
Michael Färber, David Lamprecht, Yuni Susanti
In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the…
ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering
Raphael Gruber, Abdelrahman Abdallah, Michael Färber +1
We introduce \textsc{ComplexTempQA},\footnote{Dataset and code available at: https://github.com/DataScienceUIBK/ComplexTempQA} a large-scale dataset consisting of over 100 million…
Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey
Chen Shao, Elias Giacoumidis, Syed Moktacim Billah +6
In recent years, extensive research has been conducted to explore the utilization of machine learning algorithms in various direct-detected and self-coherent short-reach communicat…
A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON
Chen Shao, Elias Giacoumidis, Shi Li +4
A frequency-calibrated SCINet (FC-SCINet) equalizer is proposed for down-stream 100G PON with 28.7 dB path loss. At 5 km, FC-SCINet improves the BER by 88.87% compared to FFE and a…
CoDy: Counterfactual Explainers for Dynamic Graphs
Zhan Qu, Daniel Gomm, Michael Färber
Temporal Graph Neural Networks (TGNNs) are widely used to model dynamic systems where relationships and features evolve over time. Although TGNNs demonstrate strong predictive capa…