Retentive Network

Retentive Networks (RetNets) are a novel neural network architecture designed to improve the efficiency and scalability of sequence modeling tasks, addressing limitations of traditional Transformer networks. Current research focuses on adapting RetNets to diverse applications, including image completion, multi-agent reinforcement learning, traffic flow prediction, and EEG signal processing, often integrating them with other architectures like convolutional neural networks and graph convolutional networks. This approach offers significant advantages in terms of inference speed, memory efficiency, and parallel training capabilities, leading to improved performance and broader applicability across various domains. The resulting advancements have implications for resource-constrained applications and large-scale data processing.