Beyond, we conduct an analysis to illustrate the tree-manner interpretability of the modular neural network. Introduction Modularity is a fundamental feature of many complex systems [1], like human brains. For example, there are certain groups of neurons in the visual cortex responding to specific...
Recurrent neural network based hybrid model for reconstructing gene regulatory network. Comput Biol Chem 2016;64:322–34. https://doi.org/10.1016/j.compbiolchem.2016.08.002.Search in Google Scholar PubMed 2. Wani, N, Raza, K. imtf-grn: integrative matrix tri-factorization for inference of...
One particular example of the latter is scene understanding, which operates under constraints imposed by the eye. The retina is characterized by a sharp decline of receptor density from the fovea to the periphery. This allows for high visual acuity, while maintaining a large field of view, but...
For example, we introduce a novel adaptive self-speculative approach based on sorted-training to accelerate large language models decoding. Moreover, SortedNet is able to train 160 sub-models at once, achieving at least 96\% of the original model's performance. PDF Abstract ...
The ability to respond to stimuli by producing force (resulting in events such as fluid motion or net displacement, in a pump or motile bioactuator, for example) is an intuitive design principle of many systems. However, a more complex biological system with greater functionality would likely ...
The NeuroSimulator performs the neural network training. The identification of a suited network architecture is supported by an automation tool. The development of modular and hierarchically organized neural networks of any degree of complexity is done in the ModuleDeveloper. Finally, the evaluation of...
It is pointed out that the neural network size and time needed for its training increase quickly with estimated parameter range and accuracy level required. Moreover, due to the local minima effect, the training process is likely to become prematurely terminated. To overcome these difficulties, a...
Examination of the artificial neural network; Analysis of the modular artificial neural network; Results obtained from example studies.FungChunCheWongKokWaiErenHalitEBSCO_AspIEEE Transactions on Instrumentation & MeasurementModular Artificial Neural Network for Prediction of Petrophysical Properties from Well ...
The assumptions include requirements that there are 30 classes which may be for example, classes of line sets associated with sonar sources. There are one hundred lines per class. Each class will have 500 "line sets" of 10 lines each. The network assumptions for the cellular automaton modules...
Formalizing Convolutional Neural Networks Consider a 1-dimensional convolutional layer with inputs {xn}{xn} and outputs {yn}{yn}: It’s relatively easy to describe the outputs in terms of the inputs: yn=A(xn,xn+1,...)yn=A(xn,xn+1,...) For example, in the above: y0=A(x0,x1)...