Track topics on Twitter Track topics that are important to you
Hopfield neural networks (HNNs) are one of the most well-known and widely used kinds of neural networks in optimization. In this article, the author focuses on building a deeper understanding of the working principle of the HNN during an optimization process. Our investigations yield several novel results giving some important insights into the working principle of both continuous and discrete HNNs. This article shows that what the traditional HNN actually does as energy function decreases is to divide the neurons into two classes in such a way that the sum of biased class volumes is minimized (or maximized) regardless of the types of the optimization problems. Introducing neuron-specific class labels, the author concludes that the traditional discrete HNN is actually a special case of the greedy asynchronous distributed interference avoidance algorithm (GADIA)  of Babadi and Tarokh for the 2-class optimization problems. The computer results confirm the findings.
This article was published in the following journal.
Name: IEEE transactions on neural networks and learning systems
Hopfield neural networks are useful for solving certain constrained set-selection problems. We establish that the vector fields associated with general networks of this type can be combined to produce...
The great achievements of deep learning can be attributed to its tremendous power of feature representation, where the representation ability comes from the nonlinear activation function and the large...
Like most machine learning algorithms, Echo State Networks possess several hyperparameters that have to be carefully tuned for achieving best performance. For minimizing the error on a specific task, ...
The architecture of convolutional neural networks (CNNs) is a key factor of influencing their performance. Although deep CNNs perform well in many difficult problems, how to intelligently design the a...
Learning long-term dependences (LTDs) with recurrent neural networks (RNNs) is challenging due to their limited internal memories. In this paper, we propose a new external memory architecture for RNNs...
The study is about the role of cellular neural networks-genetic algorithm in the diagnosis of periprosthetic hip infections. A retrospective case series of septic and aseptic loosening of ...
Project:EVO is a video-game based intervention that targets neural networks associated with cognitive control. The same networks have been implicated in poor treatment response in middle a...
Individuals with chronic insomnia have persistent difficulty falling and staying asleep, as well as complaints of altered daytime functioning that may be associated with cognitive impairme...
Renal hypoxia plays an important role in the development of contrast-induced nephropathy. The purpose of the PRINCIPLE study is to investigate the effect of pretreatment with intravenous n...
The aim of the study is to demonstrate that our semantic knowledge (elements of our long-term memory and the process we use them) respond to a graphic organisation and gather together foll...
The determination of the concentration of a given component in solution (the analyte) by addition of a liquid reagent of known strength (the titrant) until an equivalence point is reached (when the reactants are present in stoichiometric proportions). Often an indicator is added to make the equivalence point visible (e.g., a change in color).
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
An early embryonic developmental process of CHORDATES that is characterized by morphogenic movements of ECTODERM resulting in the formation of the NEURAL PLATE; the NEURAL CREST; and the NEURAL TUBE. Improper closure of the NEURAL GROOVE results in congenital NEURAL TUBE DEFECTS.
People who are in the labor force either working or looking for work for 27 weeks or more in a year, but whose income fall below a given poverty line.
The two longitudinal ridges along the PRIMITIVE STREAK appearing near the end of GASTRULATION during development of nervous system (NEURULATION). The ridges are formed by folding of NEURAL PLATE. Between the ridges is a neural groove which deepens as the fold become elevated. When the folds meet at midline, the groove becomes a closed tube, the NEURAL TUBE.