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Found 6 papers, 3 papers with code
A simple defense against adversarial attacks on heatmap explanations
With machine learning models being used for more sensitive applications, we rely on interpretability methods to prove that no discriminating attributes were used for classification.
Client Adaptation improves Federated Learning with Simulated Non-IID Clients
We present a federated learning approach for learning a client adaptable, robust model when data is non-identically and non-independently distributed (non-IID) across clients.
IROF: a low resource evaluation metric for explanation methods
The adoption of machine learning in health care hinges on the transparency of the used algorithms, necessitating the need for explanation methods.
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective.
Aggregating explanation methods for neural networks stabilizes explanations
Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation.
Aggregating explanation methods for stable and robust explainability
Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation.
