Neural network architectureFormal concept analysisOptimal NN architectureLattice-based NNSelecting an appropriate network architecture is a crucial problem when looking for a solution based on a neural network. If the number of neurons in network is too high, then it is likely to overfit....
The resulting neural network architecture has a high packing density and is well suited for very large-scale integration (VLSI). Simulation results illustrate the performance of the basic elements of a random-pulse neuron 展开 关键词: Practical, Theoretical or Mathematical/ computational complexity ...
We introduce a new architecture of information granulation-based and genetically optimized Hybrid Self-Organizing Fuzzy Polynomial Neural Networks (HSOFPNN... HS Park,W Pedrycz,SK Oh - 《Expert Systems with Applications》 被引量: 58发表: 2007年 Self-Delimiting Neural Networks Self-delimiting (SLIM)...
The concept of the dual origin of the cerebral cortex begins with the proposition that there are two prime moieties from which all cortical ... DN Pandya,EH Yeterian - 《Progress in Brain Research》 被引量: 428发表: 1990年 The chronoarchitecture of the cerebral cortex We review here a ...
Layer-wise Relevance Propagation (LRP)15is a popular method for explaining the predictions of a neural network by attributing relevance values to individual input dimensions (for example, pixels of images). In this process, relevance is propagated backwards through the network, starting from the outp...
These components can be used to build a high-level architecture which can be used to improve the quality of the system. Chen, Zhang, Li, Kang, and Yang (2009) reengineer existing legacy code to service-oriented software by identifying service candidates using RCA. Azmeh et al. (2011) use...
Out of the trialled methods, the MLP neural network architecture exhibited the highest ability to appropriately classify unseen breast cancer samples (test accuracy score ~ 97.8% underk-fold cross validation), and importantly, for the current dataset, led to no life-threatening metastatic cancer...
vectors from these matrices. Informally, this ‘contextualizes’ each vector in the sense of acknowledging the importance of other vectors surrounding it. More formally, we have a combination of linear and non-linear functions with learnable weights, so ‘just’ a specific DNN architecture. ...
Thus, there is a need for more concept extraction applications, which can aid in enhancing the explainability of neural network-based models. It offers insights for the development of knowledge bases, prompting researchers to reassess how they extract and organize concepts in order to more ...
Table E.1: Fully connected neural network architecture used as a black-box classifier in the experiments on the synthetic tabular data. nn stands for torch.nn; F stands for torch.nn.functional; input_dim corresponds to the number of input features. ...