We discuss the Bayesian decision theory on neural networks. In the two-category case where the state-conditional probabilities are normal, a three-layer neural network having d hidden layer units can approximate the posterior probability in L~p(R~d,p), where d is the dimension of the space ...
in[17]to predict micro-scale stress distributions based on heterogeneous material macro-structure. A three-dimensional implementation of this architecture was later used to predict the inelastic behavior of two-phase fiber composites[18]. Further, CNNs have been used to predict the stress field in ...
A method for optimal data simulation using random evolution operator is proposed. We consider a discrete data-driven model of the evolution operator that is a superposition of deterministic function and stochastic forcing, both parameterized with artificial neural networks (particularly, three-layer ...
The bands are used as inputs to simple 3-layer neural networks with configuration 9:3.2; 6:3.2; or 3:3.2, i.e. 9, 6, or 3 input nodes, 3 hidden nodes, and 2 output nodes. Specifically, for NN FPHW (9:3.2), Raman fluxes from 6 bands in the fingerprint region (FP) were comb...
The architecture consists of 33 layers, each consisting of 20-head multi-head self-attention73 in 1280-dimensional space followed by a single-layer MLP with GeLU activation74 and a hidden dimension of 5120. The only substantial difference between ProtBFN’s architecture and that used in ref. ...
In the case of the proposed model, the ARD mechanism is implemented by imposing an Sparse Bayesian Recurrent Neural Networks 361 appropriate hierarchical prior over the weights of the output layer connections, which results in an efficient mechanism for automatically inferring the effec- tive number ...
2.2. Radial Basis Function (RBF) Neural Network An RBF neural network is a typical three-layer neural network model with input, hidden, and output layers, as shown in Figure 1, where k is the number of input variables, H is the number of hidden neurons, I is the number of output neur...
We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different regions of the neocortex of the mouse. A set of 41 morphological variables were measured from the 3D reconstructions of real dendrites and ...
[25] proposed a generic intelligent fault detection and diagnosis strategy to simulate the actual diagnostic thinking of chiller experts, and developed a three-layer diagnostic Bayesian network to diagnose chiller faults based on the Bayesian network theory. In order to increase the diagnostic accuracy...
Unlike a three-layer feed-forward ANN that has fixed topology, the structure of a BBN can be flexibly changed for different applications, based on human knowledge. It allows investigators to specify dependence and independence of features in a natural way through the network topology. For example...