3 citations · 5 across the 4 of their papers we have counts for
4 papers
Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval
Olga Bunkova, Lorenzo Di Fruscia, Sophia Rupprecht +3
Large Language Models (LLMs) can aid synthesis planning in chemistry, but standard prompting methods often yield hallucinated or outdated suggestions. We study LLM interactions wit…
PATE: Proximity-Aware Time series anomaly Evaluation
Ramin Ghorbani, Marcel J. T. Reinders, David M. J. Tax
Evaluating anomaly detection algorithms in time series data is critical as inaccuracies can lead to flawed decision-making in various domains where real-time analytics and data-dri…
RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection
Ramin Ghorbani, Marcel J. T. Reinders, David M. J. Tax
Anomaly detection in time series data is crucial across various domains. The scarcity of labeled data for such tasks has increased the attention towards unsupervised learning metho…
Learning solutions of parametric Navier-Stokes with physics-informed neural networks
M. Naderibeni, M. J. T. Reinders, L. Wu +1
We leverage Physics-Informed Neural Networks (PINNs) to learn solution functions of parametric Navier-Stokes Equations (NSE). Our proposed approach results in a feasible optimizati…