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Found 4 papers, 1 papers with code
On the amplification of security and privacy risks by post-hoc explanations in machine learning models
A variety of explanation methods have been proposed in recent years to help users gain insights into the results returned by neural networks, which are otherwise complex and opaque black-boxes.
Automatic Concept Extraction for Concept Bottleneck-based Video Classification
CoDEx identifies a rich set of complex concept abstractions from natural language explanations of videos-obviating the need to predefine the amorphous set of concepts.
Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics
In this work, we design a novel, scalable approach, where a general demand forecasting model is built using the combined data of all the companies with a normalization factor.
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods
Explaining the inner workings of deep neural network models have received considerable attention in recent years.
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