Explainability
Decomposition
Breaks down predictions into components (e.g., Taylor Decomposition, Contextual Decomposition)
5 techniques in this subcategory
5 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| DeepLIFT | Algorithmic | Architecture/neural Networks Requirements/white Box +1 | Any | DeepLIFT (Deep Learning Important FeaTures) explains neural network predictions by decomposing the difference between... | ||
| Layer-wise Relevance Propagation | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Layer-wise Relevance Propagation (LRP) explains neural network predictions by working backwards through the network to... | ||
| Contextual Decomposition | Algorithmic | Architecture/neural Networks/recurrent Requirements/white Box +1 | Text | Contextual Decomposition explains LSTM and RNN predictions by decomposing the final hidden state into contributions from... | ||
| Taylor Decomposition | Algorithmic | Architecture/neural Networks Requirements/gradient Access +2 | Any | Taylor Decomposition is a mathematical technique that explains neural network predictions by computing first-order and... | ||
| Classical Attention Analysis in Neural Networks | Algorithmic | Architecture/neural Networks/recurrent Requirements/architecture Specific +1 | Any | Classical attention mechanisms in RNNs and CNNs create alignment matrices and temporal attention patterns that show how... |
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