applicable models
architecture specific
Requires specific architectural components to function
5 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| 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... | ||
| 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... | ||
| 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... | ||
| Monte Carlo Dropout | Algorithmic | Architecture/neural Networks Paradigm/probabilistic +4 | Any | Monte Carlo Dropout estimates prediction uncertainty by applying dropout (randomly setting neural network weights to... | ||
| Attention Visualisation in Transformers | Algorithmic | Architecture/neural Networks/transformer Requirements/architecture Specific +1 | Image Text | Attention Visualisation in Transformers analyses the multi-head self-attention mechanisms that enable transformers to... |
Rows per page
Page 1 of 1