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From Neural Activations to Concepts: A Survey on Explaining Concepts in Neural Networks
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In this paper, we review recent approaches for explaining concepts in neural networks. Concepts can act as a natural link between learning and reasoning: once the concepts are identified that a neural learning system uses, one can integrate those concepts with a reasoning system for inference or use a reasoning system to act upon them to improve or enhance the learning system. On the other hand, knowledge can not only be extracted from neural networks but concept knowledge can also be inserted into neural network architectures. Since integrating learning and reasoning is at the core of neuro-symbolic AI, the insights gained from this survey can serve as an important step towards realizing neuro-symbolic AI based on explainable concepts.
Forward citations
Cited by 2 Pith papers
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On the Performance of Concept Probing: The Influence of the Data (Extended Version)
A systematic empirical study shows concept probes need surprisingly little data for task-relevant concepts, tolerate data reuse and moderate label noise, and benefit slightly from larger probed models.
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Survival Concept-Based Learning Models
SurvCBM and SurvRCM combine concept bottleneck learning with Cox and Beran survival models, and SurvCBM achieves the best C-index and concept F1 on synthetic MNIST and CIFAR experiments.
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