23 citations · 69 across the 28 of their papers we have counts for
7 papers · 1 filter
On Adversarial Robustness of Synthetic Code Generation
Mrinal Anand, Pratik Kayal, Mayank Singh
Automatic code synthesis from natural language descriptions is a challenging task. We witness massive progress in developing code generation systems for domain-specific languages (…
NENET: An Edge Learnable Network for Link Prediction in Scene Text
Mayank Kumar Singh, Sayan Banerjee, Shubhasis Chaudhuri
Text detection in scenes based on deep neural networks have shown promising results. Instead of using word bounding box regression, recent state-of-the-art methods have started foc…
A Method for Computing Class-wise Universal Adversarial Perturbations
Tejus Gupta, Abhishek Sinha, Nupur Kumari +2
We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks. Such perturbations can induce misclassification in a large fracti…
Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification
Pratik Kayal, Mayank Singh, Pawan Goyal
The task of learning a sentiment classification model that adapts well to any target domain, different from the source domain, is a challenging problem. Majority of the existing ap…
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
Puneet Mangla, Mayank Singh, Abhishek Sinha +3
Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A recent regularization technique - M…
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
Mayank Singh, Abhishek Sinha, Nupur Kumari +3
Neural networks are vulnerable to adversarial attacks -- small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective…