Search results
Results From The WOW.Com Content Network
Instantaneously trained neural networks are feedforward artificial neural networks that create a new hidden neuron node for each novel training sample. The weights to this hidden neuron separate out not only this training sample but others that are near it, thus providing generalization.
In petroleum engineering, in situ techniques involve the application of heat or solvents to extract heavy crude oil or bitumen from reservoirs located beneath the Earth's surface. Several in situ methods exist, but those that utilize heat, particularly steam, have proven to be the most effective for oil sands extraction.
While training extremely deep (e.g., 1 million layers) neural networks might not be practical, CPU-like architectures such as pointer networks [95] and neural random-access machines [96] overcome this limitation by using external random-access memory and other components that typically belong to a computer architecture such as registers, ALU ...
In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships within the input data to create meaningful training signals. SSL tasks are designed so that solving them requires capturing essential features or relationships in the data.
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting ...
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
For one basis function, projection operator training reduces to Newton's method. Figure 6: Logistic map time series. Repeated iteration of the logistic map generates a chaotic time series. The values lie between zero and one. Displayed here are the 100 training points used to train the examples in this section.
An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network.