Israel's Multi-Layer Defense System Fully Operational Soon

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File - This Monday, Dec. 21, 2015 file photograph provided by the Israeli Ministry of Defense shows a launch of David's Sling missile defense system. A senior Israeli air force official says a joint U.S.-Israeli missile interceptor will be operational soon, completing the country's multi-layer defense system. He said Monday that David's Sling, meant to counter medium-range missiles possessed by Iranian-backed Hezbollah militants in Lebanon, will be operational in early April.

Deep Learning via Multilayer Perceptron Classifier - DZone Big Data


Deep learning which is currently a hot topic in the academia and industries tends to work better with deeper architectures and large networks. The application of deep learning in many computationally intensive problems is getting a lot of attention and a wide adoption. For example, computer vision, object recognition, image segmentation, and even machine learning classification. Some practitioners also refer to Deep learning as Deep Neural Networks (DNN), whereas a DNN is an Artificial Neural Network (ANN) with multiple hidden layers of units between the input and output layers. Similar to shallow ANNs, DNNs can model complex non-linear relationships [1]. The DNN architectures for example for object detection and parsing, generates compositional models where the object is expressed as a layered composition of image primitives. The extra layers enable composition of features from lower layers, giving the potential of modeling complex data with fewer units than a similarly performing shallow network.

On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks Machine Learning

Parsimonious representations in data modeling are ubiquitous and central for processing information. Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we herein generalize the traditional Basis Pursuit regression problem to a multi-layer setting, introducing similar sparse enforcing penalties at different representation layers in a symbiotic relation between synthesis and analysis sparse priors. We propose and analyze different iterative algorithms to solve this new problem in practice. We prove that the presented multi-layer Iterative Soft Thresholding (ML-ISTA) and multi-layer Fast ISTA (ML-FISTA) converge to the global optimum of our multi-layer formulation at a rate of $\mathcal{O}(1/k)$ and $\mathcal{O}(1/k^2)$, respectively. We further show how these algorithms effectively implement particular recurrent neural networks that generalize feed-forward architectures without any increase in the number of parameters. We demonstrate the different architectures resulting from unfolding the iterations of the proposed multi-layer pursuit algorithms, providing a principled way to construct deep recurrent CNNs from feed-forward ones. We demonstrate the emerging constructions by training them in an end-to-end manner, consistently improving the performance of classical networks without introducing extra filters or parameters.

[D] Hinton: Multi-layer neural networks should never been called MLPs • r/MachineLearning


Not sure when the term Multi-Layer Perceptron was coined (in terms of multi-layer, fully-connected, feedforward neural net with non-linear activation functions and fit via backprop), but I assume it was in the 1980s around the time of Rumelhard et al.'s backprop paper. So in that context, Perceptron referred to the linear, binary classifier that uses some kind of step-function flavor to update the weights (as opposed to the delta rule or backprop). Or in short, I think around the time the term MLP was (re?)-coined, there was only one common "Rosenblatt Perceptron"