Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of agricultural watersheds. To address this need, a team of researchers led by the ...

Built on a new architecture KumoRFM-2 achieves state-of-the-art results across 41 predictive tasks and four major benchmarks, ...

As a University assistant, you will contribute to the work group Machine Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning ...

Cyber threats are increasing in speed and complexity, driving the need for advanced detection techniques. Machine learning is ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks ...

Kumo has unveiled KumoRFM-2, a next-generation foundation model designed specifically for structured enterprise data—marking ...

ACM, the Association for Computing Machinery, today named Matei Zaharia as the recipient of the ACM Prize in Computing for ...

Online recommendation is moving into a new phase as transformers begin to reshape how graph-based systems understand users, items, and their hidden connections.

Explore how AI in high-throughput screening improves drug discovery through advanced data analysis, hit identification and ...

The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...

As enterprises adopt AI, many are discovering that context, not model sophistication, determines whether systems can be ...