The article presents Counterfactual Concept Bottleneck Models (CF-CBMs), a new class of models in deep learning. These models are designed to address three fundamental queries: predicting class labels, explaining task…
Continue readingThe research paper presents a novel uncertainty modeling framework for self-explaining neural networks. The framework aims to enhance the interpretability of deep neural networks (DNNs), which are often seen as…
Continue readingThis research paper discusses the Backpack, an alternative to the Transformer model, and its application in Chinese language modeling. The Backpack improves interpretability in English language modeling by breaking down…
Continue readingThe article introduces Prototype Generation, a robust form of feature visualisation for model-agnostic, data-independent interpretability of image classification models. It counters previous claims that feature visualisation algorithms are untrustworthy due…
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