Causal Transformer

Causal transformers are autoregressive models leveraging the transformer architecture to predict future elements in a sequence, based solely on past observations. Current research focuses on applying this framework to diverse sequential data, including robotic control, time-series analysis, and natural language processing, often employing variations like Chunking Causal Transformers or incorporating causal understanding modules to improve performance and generalization. This approach offers a powerful tool for modeling complex temporal dependencies and causal relationships, leading to advancements in various fields ranging from robotics and healthcare to cybersecurity and materials science.