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The C4 model documents the architecture of a software system, by showing multiple points of view [5] that explain the decomposition of a system into containers and components, the relationship between these elements, and, where appropriate, the relation with its users. [3] The viewpoints are organized according to their hierarchical level: [2] [3]
The use of node graph architecture in software design has recently become very popular in machine learning applications. The diagram above shows a simple neural network composed of 3 layers. The 3 layers are the input layer, the hidden layer, and the output layer. The elements in each layer are weights and are connected to weights in other layers.
An architectural model (in software) contains several diagrams representing static properties or dynamic (behavioral) properties of the software under design. [1] [2] [3] The diagrams represent different viewpoints of the system and the appropriate scope of analysis. The diagrams are created by using available standards in which the primary aim ...
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
Architecture description languages (ADLs) are used in several disciplines: system engineering, software engineering, and enterprise modelling and engineering. The system engineering community uses an architecture description language as a language and/or a conceptual model to describe and represent system architectures .
Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models, especially in processing long sequences. It is based on the Structured State Space sequence (S4) model. [2] [3] [4]
A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.
Neural architecture search (NAS) [1] [2] is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par with or outperform hand-designed architectures.