2 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
How do SGD hyperparameters in natural training affect adversarial robustness?
Sandesh Kamath, Amit Deshpande, K V Subrahmanyam
Learning rate, batch size and momentum are three important hyperparameters in the SGD algorithm. It is known from the work of Jastrzebski et al. arXiv:1711.04623 that large batch s…
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…
Universalization of any adversarial attack using very few test examples
Sandesh Kamath, Amit Deshpande, K V Subrahmanyam +1
Deep learning models are known to be vulnerable not only to input-dependent adversarial attacks but also to input-agnostic or universal adversarial attacks. Dezfooli et al. \cite{D…
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks
Sandesh Kamath, Amit Deshpande, K V Subrahmanyam +1
(Non-)robustness of neural networks to small, adversarial pixel-wise perturbations, and as more recently shown, to even random spatial transformations (e.g., translations, rotation…