2 citations · 3 across the 3 of their papers we have counts for
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Rethinking Robustness of Model Attributions
Sandesh Kamath, Sankalp Mittal, Amit Deshpande +1
For machine learning models to be reliable and trustworthy, their decisions must be interpretable. As these models find increasing use in safety-critical applications, it is import…
On the Robustness of Explanations of Deep Neural Network Models: A Survey
Amlan Jyoti, Karthik Balaji Ganesh, Manoj Gayala +3
Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) model…
On Universalized Adversarial and Invariant Perturbations
Sandesh Kamath, Amit Deshpande, K V Subrahmanyam
Convolutional neural networks or standard CNNs (StdCNNs) are translation-equivariant models that achieve translation invariance when trained on data augmented with sufficient trans…