A LATERALLY CONNECTED NEURAL NETWORK MODEL Many of the currently used feed forward or recurrent neural network models have little, if any, structurally constrained architecture which is contrary to ... F Azam,HF Vanlandingham - Iasted International Conference on Neural Networks Nn May 被引量: 0发...
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....
However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, ...
The importance of such a theory and the underlying universal principles is difficult to overestimate. For example, the design of modern artificial neural networks (ANNs) copies the converging architecture of biological sensory systems6. As a result, they already outperform humans in pattern recognition...
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. ...
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...
Concept lattice theory has also been employed to tackle key challenges in neural network architecture. In order to overcome difficulties such as overfitting and poor interpretability, Kuznetsov et al. [24] proposed an approach to constructing neural networks based on concept lattices and on lattices ...
Hence, this complex behavior of humans opens up the avenues of neural network artificial intelligence for tracking and predicting the neuromarketing phenomenon [53,57,92]. The neural network artificial intelligence gauge the neural activity of the human brain [52,54,93] regarding the effectiveness ...
In the analogy of artificial neural networks, different networks will tend to have different “internal representations” because this depends not only on the network architecture, but also on the particular training data that the network has “experienced”. Why can’t one human consciousness “get...
Section 3 briefly describes the structure of the VEBF neural network and how to compute the orthonormal basis for the axes rotation along the direction of a dataset. Section 4 presents the concept of Class-wise Incremental learning CIL with VEBF neurons. Section 5 discusses the model evaluation...