Explainability
Gradient Based
Uses gradients/derivatives to compute feature importance (e.g., Integrated Gradients, Saliency Maps)
7 techniques in this subcategory
7 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Integrated Gradients | Algorithmic | Architecture/neural Networks Paradigm/parametric +3 | Any | Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of... | ||
| DeepLIFT | Algorithmic | Architecture/neural Networks Requirements/white Box +1 | Any | DeepLIFT (Deep Learning Important FeaTures) explains neural network predictions by decomposing the difference between... | ||
| 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... | ||
| Saliency Maps | Algorithmic | Architecture/neural Networks Requirements/differentiable +1 | Image | Saliency maps are visual explanations for image classification models that highlight which pixels in an image most... | ||
| Gradient-weighted Class Activation Mapping | Algorithmic | Architecture/neural Networks/convolutional Requirements/architecture Specific +2 | Image | Grad-CAM creates visual heatmaps showing which regions of an image a convolutional neural network focuses on when making... | ||
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +6 | Any | Influence functions quantify how much each training example influenced a model's predictions by computing the change in... | ||
| Feature Attribution with Integrated Gradients in NLP | Algorithmic | Architecture/neural Networks/transformer Architecture/neural Networks/transformer/llm +4 | Text | Applies Integrated Gradients to natural language processing models to attribute prediction importance to individual... |
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