Global ETD Search
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Showing 1 to 4 of 4 for “"concept-based explanations"”.
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Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations
… to improve interpretability. Feature importance explanations are the most popular interpretability approaches. They show the importance of each input feature (e.g., pixel, patch, word vector) to the model’s prediction. However, we hypothesise that feature importance explanations have two main …
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Enhancing Interpretability: The Role of Concept-based Explanations Across Data Types
… Thus, a key step to further empowering DNN-based approaches is improving their explainability, which is addressed by the field of Explainable AI (XAI). Recently, Concept-based explanations (CbEs) have emerged as a powerful new XAI paradigm, providing model explanations in terms of …
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Interpretability of Neural Networks Latent Representations
… methods: feature importance, example-based and concept-based explanations. Most feature importance methods require a label to select which component of the neural network output to interpret. When interpreting neural networks representations, components corresponds to neuron …
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From local explanations to comprehensive mechanistic understanding of deep vision models
… rely on manual inspection of individual explanations, they fail to scale with the size and complexity of today’s models and datasets. This dissertation develops an explainability framework that is (i) mechanistic, by providing component-level insights (ii) comprehensive, by integrating …