Tag: multi-head attention

A simulation study reports that a distributed imitation learning framework with adversarial perturbation training cut path tracking error to 0.18 ...

A three-stage CNN and transformer pipeline identified satellite image types and detected oil with high reported accuracy, while a lightweight ...

A supervised graph-learning model combining metapath embeddings, graph convolution and attention outperformed 12 baselines on the DBLP and ACM academic-network ...

A deep-learning framework classified sleep stages from single-channel EEG with reported accuracies of 84.5% to 87.0% across three public datasets, ...

A deep reinforcement learning framework with a novel dynamic attention mechanism outperforms traditional methods on static and dynamic container relocation ...

A new encoder–decoder transformer with waveform-level data augmentation achieves 100 percent accuracy on German emotional speech and 94 percent on ...

Researchers have built a transformer-based intrusion detection framework that models relationships among network traffic features and outperforms nine baseline AI ...

Researchers have developed GTVelo, a graph transformer-based neural ordinary differential equation model that improves RNA velocity inference by capturing long-range ...

Researchers in Morocco have developed IACAN, a deep learning architecture that dynamically balances convolutional and attention branches using KL divergence ...