An evolutionary sampling scheme for generating reference structures improves the NNs' mapping of regions visited in unconstrained searches, while a stratified training approach enables the creation of standardized NN models for multiple elements. A more flexible NN architecture proposed here expands the ...
Importantly, by identifying a smooth and monotonous relationship between structural and functional neural network architecture it was possible to devise a network fitting algorithm that allows to simultaneously and precisely control the state of synchronization between every pair of network nodes, allowing ...
We propose a reconfigurable neural network structure which has capability to process supervised or unsupervised learning algorithm computation. The proposed structure is based on modular structure which can configure artificial neural network architecture flexibly. Main processing unit of the proposed structure...
Third, graph modeling has motivated a broad range of studies that summarize organizational principles of SC or FC6,7,8, allowing to extract systems-level network properties of architecture, evolution, development, and alterations by disease or disorder9,10. Finally, in order to probe the causal ...
TLDR: We study the architecture of neural networks through the lens of network science, and discover thatgood neural networks are alikein terms of their underlying graph structure. We define a novel graph-based representation of neural networks calledrelational graph, as opposed to the commonly used...
Structure learning refers to the process of designing methods to learn the appropriate structure of a model, such as Sum-Product Networks (SPNs), in order to apply them effectively in practical applications. AI generated definition based on: Neural Networks, 2023 ...
In this section we present the architecture of the neural network model we use to generate shared feature-structure node embeddings.Footnote 1 We take a featured network as input, with structure represented as an adjacency matrix and node features represented as vectors (see below for a formal de...
CONSTRUCTIVE TRAINING ALGORITHM FOR DESIGNING FEEDFORWARD NEURAL NETWORKS: A REVIEW In this paper, we review neural networks, models of neural networks, methods for selecting neural network architecture and constructive algorithms for regression problems. Cascade correlation algorithm is the most suitable fo...
The architecture of the proposed autoencoder is detailed in “Neural network architecture” and in Supplementary Note 5. Network architecture in the SI. Training and evaluation procedures We trained and evaluated the autoencoder and ICSG3D25 (baseline) on three materials datasets: ICSG3D, limited ...
We first describe the organizing principles of brain network architecture instantiated in structural wiring under constraints of spatial embedding and energy minimization. We then survey models of brain network function that stipulate how neural activity propagates along structural connections. Finally, we ...