Effective fraud detection in blockchain relies heavily on the performance of Graph Neural Networks, but achieving optimal results requires careful attention to initialization and normalization techniques.
New research reveals how to trace the origins of large language model responses – whether they stem from learned knowledge or provided context.
![RhythmBERT offers a novel approach to understanding temporal patterns, embedding rhythmic information directly into the BERT architecture to capture nuanced sequential dependencies beyond those identified by standard models [latex] BERT [/latex].](https://arxiv.org/html/2602.23060v1/2602.23060v1/x1.png)
Researchers are applying the principles of natural language processing to electrocardiogram (ECG) data, creating models that ‘understand’ heart rhythms and improve disease detection.
A new approach combines data-driven deep learning with established epidemiological principles to improve the accuracy of outbreak predictions.
As artificial intelligence systems become increasingly complex, effectively managing inherent uncertainty is crucial for safe and dependable operation.
A new approach leveraging graph neural networks and spatial clustering dramatically improves how we model and optimize interconnected energy systems.
New research tackles the challenge of accurately forecasting human movement, even when data is incomplete or noisy.
![Circuit accuracy, as measured by [latex] cACC [/latex], demonstrates a correlation with circuit size [latex] KK [/latex], with certified circuits consistently outperforming baseline models across diverse datasets and scoring methods when evaluated on out-of-distribution data, suggesting a robust relationship between circuit complexity and generalization capability.](https://arxiv.org/html/2602.22968v1/2602.22968v1/x1.png)
Researchers have developed a method to rigorously verify the stability of identified neural network circuits, enhancing their trustworthiness and predictive power.
A new hardware generator efficiently compresses sparse data streams, unlocking the potential of graph neural networks for high-speed data analysis in particle physics and beyond.
A new approach combines physics-based modeling with machine learning to detect and diagnose faults in complex thermal-hydraulic processes.