TransformerFAM: Feedback attention is working memory

arXiv:2404.09173v1 Announce Type: new
Abstract: While Transformers have revolutionized deep learning, their quadratic attention complexity hinders their ability to process infinitely long inputs. We propose Feedback Attention Memory (FAM), a novel Transformer architecture that leverages a feedback loop to enable the network to attend to its own latent representations. This design fosters the emergence of working memory within the Transformer, allowing it to process indefinitely long sequences. TransformerFAM requires no additional weights, enabling seamless integration with pre-trained models. Our experiments …

abstract architecture arxiv attention complexity cs.ai cs.cl cs.lg deep learning design emergence feedback inputs loop memory network novel process transformer transformer architecture transformers type