27 citations · 120 across the 14 of their papers we have counts for
16 papers
Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective Genetic Optimization Guided By Deep Networks
Alex Mathai, Shreya Khare, Srikanth Tamilselvam +1
We propose a novel genetic-algorithm technique that generates black-box adversarial examples which successfully fool neural network based text classifiers. We perform a genetic sea…
Evaluation of Siamese Networks for Semantic Code Search
Raunak Sinha, Utkarsh Desai, Srikanth Tamilselvam +1
With the increase in the number of open repositories and discussion forums, the use of natural language for semantic code search has become increasingly common. The accuracy of the…
Reducing Overlearning through Disentangled Representations by Suppressing Unknown Tasks
Naveen Panwar, Tarun Tater, Anush Sankaran +1
Existing deep learning approaches for learning visual features tend to overlearn and extract more information than what is required for the task at hand. From a privacy preservatio…
Benchmarking Popular Classification Models' Robustness to Random and Targeted Corruptions
Utkarsh Desai, Srikanth Tamilselvam, Jassimran Kaur +2
Text classification models, especially neural networks based models, have reached very high accuracy on many popular benchmark datasets. Yet, such models when deployed in real worl…
Coverage Testing of Deep Learning Models using Dataset Characterization
Senthil Mani, Anush Sankaran, Srikanth Tamilselvam +1
Deep Neural Networks (DNNs), with its promising performance, are being increasingly used in safety critical applications such as autonomous driving, cancer detection, and secure au…
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
Ameya Prabhu, Riddhiman Dasgupta, Anush Sankaran +2
For an unknown (new) classification dataset, choosing an appropriate deep learning architecture is often a recursive, time-taking, and laborious process. In this research, we propo…