12 citations · 16 across the 7 of their papers we have counts for
9 papers
Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction
Abhijith Sharma, Phil Munz, Apurva Narayan
Adversarial patches threaten visual AI models in the real world. The number of patches in a patch attack is variable and determines the attack's potency in a specific environment.…
NSA: Naturalistic Support Artifact to Boost Network Confidence
Abhijith Sharma, Phil Munz, Apurva Narayan
Visual AI systems are vulnerable to natural and synthetic physical corruption in the real-world. Such corruption often arises unexpectedly and alters the model's performance. In re…
Do we need entire training data for adversarial training?
Vipul Gupta, Apurva Narayan
Deep Neural Networks (DNNs) are being used to solve a wide range of problems in many domains including safety-critical domains like self-driving cars and medical imagery. DNNs suff…
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey
Abhijith Sharma, Yijun Bian, Phil Munz +1
Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security a…
Soft Adversarial Training Can Retain Natural Accuracy
Abhijith Sharma, Apurva Narayan
Adversarial training for neural networks has been in the limelight in recent years. The advancement in neural network architectures over the last decade has led to significant impr…
Spiking Approximations of the MaxPooling Operation in Deep SNNs
Ramashish Gaurav, Bryan Tripp, Apurva Narayan
Spiking Neural Networks (SNNs) are an emerging domain of biologically inspired neural networks that have shown promise for low-power AI. A number of methods exist for building deep…
HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings
Maulik Parmar, Apurva Narayan
Hypernymy plays a fundamental role in many AI tasks like taxonomy learning, ontology learning, etc. This has motivated the development of many automatic identification methods for…
A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study
Milad Ramezankhani, Amir Nazemi, Apurva Narayan +4
Despite the successful implementations of physics-informed neural networks in different scientific domains, it has been shown that for complex nonlinear systems, achieving an accur…
Deep Learning for System Trace Restoration
Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau +1
Most real-world datasets, and particularly those collected from physical systems, are full of noise, packet loss, and other imperfections. However, most specification mining, anoma…