Tensorized Neural Network

Tensorized neural networks (TNNs) aim to improve the efficiency and performance of standard neural networks by representing their weight matrices as tensors. Current research focuses on applying this approach to various architectures, including convolutional and recurrent networks, and autoencoders, often incorporating techniques like attention mechanisms and contrastive learning to enhance performance. This approach addresses challenges like high computational cost and large model sizes, leading to significant reductions in resource consumption while maintaining, or even improving, accuracy in applications such as image processing, natural language processing, and weather forecasting. The resulting efficiency gains are particularly impactful for deployment on resource-constrained devices.