Add to ORKGThe thesis studies different kind of adversarial attacks on Convolutional Neural Network by using electric load data set in order to fool deep neural network. With the improvement of Deep Learning methods, their securities and vulnerabilities have become an important research subject. An adversary who gains access to the model and data sets may add some perturbations to the datasets, which may cause significant damage to the system. By using adversarial attacks, it shows how much these attacks affect the system and shows the attacks\u27 success in this research
Abstract: Background: From Previous research, state-of-the-art deep neural networks have accomplishe...
Deep neural networks are making their way into our everyday lives at an increasing rate. While the a...
DeepNeuralNetworks (DNNs) are powerful to the classification tasks, finding the potential links bet...
The thesis studies different kind of adversarial attacks on Convolutional Neural Network by using el...
Deep learning has witnessed astonishing advancement in the last decade and revolutionized many field...
Despite superior accuracy on most vision recognition tasks, deep neural networks are susceptible to ...
Convolutional Neural Networks (CNNs) have been at the frontier of the revolution within the field of...
Despite the overwhelming success of neural networks for pattern recognition, these models behave cat...
With the advancement of accelerated hardware in recent years, there has been a surge in the developm...
With recent advancements in the field of artificial intelligence, deep learning has created a niche ...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...
Deep learning plays an important role in various disciplines, such as auto-driving, information tech...
In the past few years, evaluating on adversarial examples has become a standard procedure to meas...
Thesis (Master's)--University of Washington, 2017-06Convolutional Neural Networks and Deep Learning ...
Part 1: Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT)International audienceDee...
Abstract: Background: From Previous research, state-of-the-art deep neural networks have accomplishe...
Deep neural networks are making their way into our everyday lives at an increasing rate. While the a...
DeepNeuralNetworks (DNNs) are powerful to the classification tasks, finding the potential links bet...
The thesis studies different kind of adversarial attacks on Convolutional Neural Network by using el...
Deep learning has witnessed astonishing advancement in the last decade and revolutionized many field...
Despite superior accuracy on most vision recognition tasks, deep neural networks are susceptible to ...
Convolutional Neural Networks (CNNs) have been at the frontier of the revolution within the field of...
Despite the overwhelming success of neural networks for pattern recognition, these models behave cat...
With the advancement of accelerated hardware in recent years, there has been a surge in the developm...
With recent advancements in the field of artificial intelligence, deep learning has created a niche ...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...
Deep learning plays an important role in various disciplines, such as auto-driving, information tech...
In the past few years, evaluating on adversarial examples has become a standard procedure to meas...
Thesis (Master's)--University of Washington, 2017-06Convolutional Neural Networks and Deep Learning ...
Part 1: Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT)International audienceDee...
Abstract: Background: From Previous research, state-of-the-art deep neural networks have accomplishe...
Deep neural networks are making their way into our everyday lives at an increasing rate. While the a...
DeepNeuralNetworks (DNNs) are powerful to the classification tasks, finding the potential links bet...