Graph Convolutional Network (GCN): A neural network architecture ... Reinforcement Learning (RL): A type of machine learning where an agent learns to make decisions by receiving rewards or ...

We estimate missing traffic flow based on the proposed feature set by utilizing five state-of-the-art machine learning and deep learning methods, including KNN, Random Forest, XGBoost, Graph ...

Specifically, GCN relies on the calculation of the adjacency matrix ... Training loss and verification loss between different algorithms on Cora dataset. t-SNE is a machine learning algorithm for data ...

author={Lei, Huan and Akhtar, Naveed and Mian, Ajmal}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2020} } @article{lei2019octree, title={Octree guided CNN with ...

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...

Combining concepts from statistical physics with machine learning, researchers at the University of Bayreuth have shown that ...

Enzymes significantly speed up the chemical reactions that keep you alive. Researchers are using AI to create new ones to tackle modern challenges.

Research on climate policy is growing exponentially. Of the approximately 85,000 individual studies ever published on policy ...

One of the best ways to reduce your vulnerability to data theft or privacy invasions when using large language model artificial intelligence or machine learning, is to run the model locally.

Researchers have explored how integrating machine and deep learning techniques can create a standardized system for evaluating rock climbing routes to provide a difficulty grading scale that promotes ...

Accelerated Climate Modeling AI-driven models, particularly Google's NeuralGCM, are leading a revolution in climate modeling.