3 Result(s)
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Chapter and Conference Paper
A Novel Explainable Deep Learning Model with Class Specific Features
The predictive accuracy of any machine learning model is highly depended on the features used to train the model. For this reason, it is important to extract good discriminative features from the raw data. Thi...
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Chapter and Conference Paper
Explaining Black-Box Models Using Interpretable Surrogates
Explaining black-box machine learning models is important for their successful applicability to many real world problems. Existing approaches to model explanation either focus on explaining a particular decisi...
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Chapter and Conference Paper
Detection of Compromised Models Using Bayesian Optimization
Modern AI is largely driven by machine learning. Recent machine learning algorithms such as deep neural networks (DNN) have become quite effective in many recognition tasks e.g., object recognition, face recog...
