35stdnn13

35stdnn13 is a designation used in artificial intelligence literature and internal project files to refer to a family of deep neural network configurations. The label does not correspond to a single fixed model; rather it functions as a codename employed by researchers to group related architectures under a common naming scheme.

Origin and meaning: The components of the name are interpreted as follows: '35' may indicate a versioning or a parameter count; 'std' suggests standard or modular design; 'nn' stands for neural network; '13' is a sub-variant or release number. Interpretations vary by organization.

Design and variants: The 35stdnn13 family covers multiple architectures, typically built from blocks of convolutional or transformer layers, with common techniques such as skip connections and normalization. Variants may emphasize efficiency, accuracy, or low-resource deployment.

Training and data: Models in this family are typically trained on standard image or signal datasets, using common optimization approaches (e.g., AdamW, SGD with momentum) and augmentations. Training regimes and hyperparameters differ by project.

Reception and use: The designation appears mainly in internal reports and a small subset of publications; there is no formal standard definition. In public literature, it is often cited as an example of naming practices for neural networks rather than as a specific model.

See also: neural network architectures, model naming conventions, deep learning benchmarks.