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 standardi
TLDR: We study the architecture of neural networks through the lens of network science, and discover that good neural networks are alike in terms of their underlying graph structure.We define a novel graph-based representation of neural networks called relational graph, as opposed to the commonly ...
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 ...
Neural-network quantum states have been successfully used to study a variety of lattice and continuous-space problems. Despite a great deal of general methodological developments, representing fermionic matter is however still early research activity. He
Structure and parameter learningHybrid evolutionary gradient descentThis paper provides a solution to select a suitable architecture of convolutional neural network (CNN). A hybrid evolutionary gradient descent (HyEGD) approach is proposed to automatically evolve the......
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 ...
Figure 3: The overall architecture of BRCNN. Two-Channel recurrentneural networkswith LSTM units pick up information along the shortest dependency path, and inversely at the same time. Convolution layers are applied to extract local features from the dependency units. In the example, we conduct ...
Deepstruct can automatically create a deep neural network models based on graphs and for purposes of visualization, analysis or transformations it also supports graph extraction from a given model.Interested in neural network visualizations, pruning, neural architecture search or neural structure in ...
Since a neural network is composed of both nodes and edges, it would be intuitive to set up the optimization problem in terms of the edge weights, the structure of the network, and the pruning strategy. Based on the hierarchical characteristic of the neural network structure, pruning techniques...
The winning article wasMapping the neural circuitry of predator fear in the nonhuman primateby Quentin Montardyet al. The runner up prize went toMapping the living mouse brain neural architecture: strain-specific patterns of brain structural and functional connectivityby Meltem Karataset al. ...