73 research outputs found
Automated steering design using Neural Network
If you don't move forward-you begin to move backward.
Technological advancement today has brought us to a frontier where the human has become the basic constraint in our ascent towards safer and faster transportation. Human error is mostly responsible for many road traffic accidents which every year take the lives of lots of people and injure many more. Driving protection is thus a major concern leading to research in autonomous driving systems.
Automatic motion planning and navigation is the primary task of an automated guided vehicle or mobile robots. All such navigation systems consist of a data collection system, a decision making system and a hardware control system. In this research our artificial intelligence system is based on neural network model for navigation of an AGV in unpredictable and imprecise environment. A five layered with gradient descent momentum back-propagation system which uses heading angle and obstacle distances as input.
The networks are trained by real data obtained from vehicle tracking live test runs. Considering the high amount of risk of testing the vehicle in real space-time conditions, it would initially be tested in simulated environment with the use of MATLAB®. The hardware control for an AGV should be robust and precise. An Aerial and a Grounded prototype were developed to test our neural network model in real time situation
Taming GANs with Lookahead
Generative Adversarial Networks are notoriously challenging to train. The
underlying minimax optimization is highly susceptible to the variance of the
stochastic gradient and the rotational component of the associated game vector
field. We empirically demonstrate the effectiveness of the Lookahead
meta-optimization method for optimizing games, originally proposed for standard
minimization. The backtracking step of Lookahead naturally handles the
rotational game dynamics, which in turn enables the gradient ascent descent
method to converge on challenging toy games often analyzed in the literature.
Moreover, it implicitly handles high variance without using large mini-batches,
known to be essential for reaching state of the art performance. Experimental
results on MNIST, SVHN, and CIFAR-10, demonstrate a clear advantage of
combining Lookahead with Adam or extragradient, in terms of performance, memory
footprint, and improved stability. Using 30-fold fewer parameters and 16-fold
smaller minibatches we outperform the reported performance of the
class-dependent BigGAN on CIFAR-10 by obtaining FID of \emph{without}
using the class labels, bringing state-of-the-art GAN training within reach of
common computational resources
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
Federated learning is a machine learning paradigm that emerges as a solution
to the privacy-preservation demands in artificial intelligence. As machine
learning, federated learning is threatened by adversarial attacks against the
integrity of the learning model and the privacy of data via a distributed
approach to tackle local and global learning. This weak point is exacerbated by
the inaccessibility of data in federated learning, which makes harder the
protection against adversarial attacks and evidences the need to furtherance
the research on defence methods to make federated learning a real solution for
safeguarding data privacy. In this paper, we present an extensive review of the
threats of federated learning, as well as as their corresponding
countermeasures, attacks versus defences. This survey provides a taxonomy of
adversarial attacks and a taxonomy of defence methods that depict a general
picture of this vulnerability of federated learning and how to overcome it.
Likewise, we expound guidelines for selecting the most adequate defence method
according to the category of the adversarial attack. Besides, we carry out an
extensive experimental study from which we draw further conclusions about the
behaviour of attacks and defences and the guidelines for selecting the most
adequate defence method according to the category of the adversarial attack.
This study is finished leading to meditated learned lessons and challenges
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