Interpretability Techniques for Deep Learning
11 papers with code • 1 benchmarks • 1 datasets
CausalGym
Latest papers with no code
ProvFL: Client-Driven Interpretability of Global Model Predictions in Federated Learning
Regardless of the quality of the global model or if it has a fault, understanding the model's origin is equally important for debugging, interpretability, and explainability in federated learning.
Improving Interpretability via Regularization of Neural Activation Sensitivity
We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques.
A deep supervised learning approach for condition-based maintenance of naval propulsion systems Tarek
In the last years, predictive maintenance has gained a central position in condition-based maintenance tasks planning.
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics
We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well.
