Decoder Model

Decoder models, a class of neural networks that generate sequential outputs, are central to many applications including natural language processing and neural decoding of brain signals. Current research focuses on improving their efficiency, robustness (e.g., to dialectal variations or inter-subject variability in neuroimaging data), and mitigating biases, often employing transformer architectures and techniques like contrastive learning or unlearning. These advancements are significant for enhancing the performance and reliability of various AI systems and for gaining deeper insights into complex data, such as brain activity.