6 papers
BIM-Edit: Benchmarking Large Language Models for IFC-Based Building Information Modeling
Bharathi Kannan Nithyanantham, Clemens Kujat, Tobias Sesterhenn +5
Large language models (LLMs) are increasingly applied to computer-aided design (CAD) to generate design artifacts from textual instructions. In engineering practice, this requires…
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Andrej Tschalzev, Nick Erickson, Yuyang Wang +4
Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remains a critical yet underexplor…
Where to Measure: Epistemic Uncertainty-Based Sensor Placement with ConvCNPs
Feyza Eksen, Stefan Oehmcke, Stefan Lüdtke
Accurate sensor placement is critical for modeling spatio-temporal systems such as environmental and climate processes. Neural Processes (NPs), particularly Convolutional Condition…
MCP4IFC: IFC-Based Building Design Using Large Language Models
Bharathi Kannan Nithyanantham, Tobias Sesterhenn, Ashwin Nedungadi +4
Bringing generative AI into the architecture, engineering and construction (AEC) field requires systems that can translate natural language instructions into actions on standardize…
Informed, but Not Always Improved: Challenging the Benefit of Background Knowledge in GNNs
KutalmıŠCoÅkun, Ivo Kavisanczki, Amin Mirzaei +4
In complex and low-data domains such as biomedical research, incorporating background knowledge (BK) graphs, such as protein-protein interaction (PPI) networks, into graph-based ma…
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality
Andrej Tschalzev, Lennart Purucker, Stefan Lüdtke +3
Data repositories have accumulated a large number of tabular datasets from various domains. Machine Learning researchers are actively using these datasets to evaluate novel approac…