No abstract

No abstract

No abstract

Sparsity in the structure of Neural Networks can lead to less energy consumption, less memory usage, faster computation times on convenient hardware, and automated machine learning. If sparsity gives rise to certain kinds of structure, it can explain automatically obtained features during learning.We provide insights into experiments in which we show how sparsity can be achieved through prior initialization, pruning, and during learning, and answer questions on the relationship between the structure of Neural Networks and their performance. This includes the first work of inducing priors from network theory into Recurrent Neural Networks and an architectural performance prediction during a Neural Architecture Search. Within our experiments, we show how magnitude class blinded pruning achieves 97.5% on MNIST with 80% compression and re-training, which is 0.5 points more than without compression, that magnitude class uniform pruning is significantly inferior to it and how a genetic search enhanced with performance prediction achieves 82.4% on CIFAR10. Further, performance prediction for Recurrent Networks learning the Reber grammar shows an R 2 of up to 0.81 given only structural information.Preprint. Under review.

Abstract-There have been many changes in the ways people live, work, play and share in the past three decades due to introduction of Information Technology. Information Technology has enormous impact on various domains like business, educational fields, healthcare departments, and also the defense department. This also introduced a risk of information theft and vital archive leaks. For these reasons, it is now necessary to secure such information using various encryption methods available. We can improve the security level of such encryption methods by making them location aware. This will add an extra layer to the security of the crucial data.