QILIS, or Quantum-Inspired Lifecycle Interpretability System, is a framework for providing interpretability across the full lifecycle of neural network models. It combines quantum-inspired metrics, semantic evaluation, and dynamic optimization to ensure models remain transparent, efficient, and explainable from training through inference and analysis.
Key components include:
* DRMP for propagating relevance metrics like mutual information, cosine similarity, and purity across layers and phases.
* AMSE for maintaining semantic coherence of features.
* RBCO for dynamically pruning low-relevance features to improve efficiency.
* A knowledge base for storing and retrieving feature relevance data.
* An interpretive output generator for creating human-readable explanations.
QILIS supports various architectures, including CNNs, RNNs, and transformers, and is especially suited for high-stakes applications such as healthcare and finance.
