Abstract: Most existing coal mill fault warning methods fail to explicitly model the spatial dependencies among variables during the modeling process, thereby reducing the accuracy of feature ...

1 Department of Computer Science, Mountains of the Moon University, Fortportal, Uganda. 2 Department of Computer Science and Informatics, University of Nairobi, Nairobi, Kenya. 3 Department of ...

Abstract: Temporal knowledge graph (TKG) reasoning involves inferring future unknown facts based on historical data. Current approaches to temporal reasoning can be broadly categorized into two main ...

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated. MIT Technology Review Explains: Let our writers untangle the complex, messy ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

Neuroscience is a multidisciplinary science that is concerned with the study of the structure and function of the nervous system. It encompasses the evolution, development, cellular and molecular ...

This modelling study tests several hypotheses describing how seasonality and migration drive the epidemiology of Rift Valley Fever Virus among transhumant cattle in The Gambia. The work is ...

This codebase contains training and evaluation scripts for Flock, including pretraining scripts and checkpoints on FB15k-237, WN18RR, and CoDEx Medium, zero-shot and finetuning evaluation over 54 ...

A Python package that implements multivariate Hawkes-style spatio‑temporal point processes on networks with deep kernels parameterized by Graph Neural Networks (GNNs). The CLI allows training from a ...