Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Narayan, Apurva"'
Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodologies. Recent studies have revealed that, like their classical counterp
Externí odkaz:
http://arxiv.org/abs/2402.08648
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. Most existing defenses assume a single patch in the scene, and the mul
Externí odkaz:
http://arxiv.org/abs/2311.01563
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 recent years, the primary focus has been on adversarial attacks. However
Externí odkaz:
http://arxiv.org/abs/2307.14917
Autor:
Gupta, Vipul, Narayan, Apurva
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 suffer from vulnerability against adversarial attacks. In the past few yea
Externí odkaz:
http://arxiv.org/abs/2303.06241
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 and robustness of AI models. Yet the more primitive adversarial attacks
Externí odkaz:
http://arxiv.org/abs/2206.08304
Autor:
Sharma, Abhijith, Narayan, Apurva
Publikováno v:
In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART 2022
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 improvement in their performance. It sparked an interest in their deployme
Externí odkaz:
http://arxiv.org/abs/2206.01904
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 SNNs, with Artificial Neural Network (ANN)-to-SNN conversion being hi
Externí odkaz:
http://arxiv.org/abs/2205.07076
Autor:
Ramezankhani, Milad, Nazemi, Amir, Narayan, Apurva, Voggenreiter, Heinz, Harandi, Mehrtash, Seethaler, Rudolf, Milani, Abbas S.
Despite the successful implementations of physics-informed neural networks in different scientific domains, it has been shown that for complex nonlinear systems, achieving an accurate model requires extensive hyperparameter tuning, network architectu
Externí odkaz:
http://arxiv.org/abs/2202.06139
Publikováno v:
In Computers in Industry October 2023 151
Most real-world datasets, and particularly those collected from physical systems, are full of noise, packet loss, and other imperfections. However, most specification mining, anomaly detection and other such algorithms assume, or even require, perfec
Externí odkaz:
http://arxiv.org/abs/1904.05411