Intermediate Representation

Intermediate representations (IRs) are internal data structures within machine learning models that capture information extracted from input data at various processing stages. Current research focuses on leveraging IRs to improve model performance, interpretability, and robustness across diverse applications, including image retrieval, code generation, and question answering. This involves exploring different IR designs, such as graph-based representations or wavelet-transformed embeddings, and employing various architectures like transformers and neural networks to process and utilize these representations. The effective use of IRs is crucial for advancing model capabilities and addressing challenges like generalization, privacy, and explainability in machine learning.