applicable models Filters
Browse and search through available filters in this category. Click on any filter to view related techniques.
| Description | Action | ||
|---|---|---|---|
black box | Only requires input-output access, no internal model access needed | 114 | |
model agnostic | Works with any model type without requiring specific architecture (black-box techniques) | 109 | |
supervised | Requires labelled training data | 51 | |
white box | Requires full model transparency and access | 39 | |
neural networks | Techniques for general neural network architectures | 31 | |
llm | Techniques for Large Language Models (GPT, BERT, etc.) | 19 | |
gradient access | Requires access to model gradients | 18 | |
model internals | Requires access to weights, neurons, or internal representations | 17 | |
generative | Models that learn data distributions | 14 | |
parametric | Models with a fixed number of parameters | 12 | |
probabilistic output | Model must provide probability distributions as output | 10 | |
discriminative | Models that learn decision boundaries directly | 9 | |
differentiable | Model must be differentiable | 9 | |
linear models | Techniques for linear and generalized linear models | 5 | |
transformer | Techniques for transformer-based architectures | 5 | |
unsupervised | Works with unlabeled data | 5 | |
tree based | Techniques for tree-based algorithms (decision trees, random forests, gradient boosting) | 4 | |
architecture specific | Requires specific architectural components to function | 4 | |
recurrent | Techniques for RNNs, LSTMs, and GRUs | 3 | |
logistic | Techniques for logistic regression models | 2 |
Showing 20 of 34 filters