modelarchitecturen

Modelarchitecturen refers to the structure of a model, including how its components are arranged and how information flows from input to output. In AI and data science, a model’s architecture defines the inductive biases, computational requirements, and the kinds of problems it is best suited to solve. Architecture is distinct from a model’s parameters: the architecture is the design, while the parameters are the values learned during training.

Common architectures in machine learning include neural networks such as feedforward networks for basic mappings, convolutional neural networks for image and spatial data, recurrent networks for sequential data, and transformer-based models that rely on attention mechanisms. Graph neural networks extend models to graph-structured data. In probabilistic modeling, architectural choices specify hierarchical or autoregressive structures, factor graphs, or Bayesian networks. There is also a rise of modular and hybrid architectures that combine different components, as well as architectures designed for multimodal data and efficient deployment.

Design choices matter and involve trade-offs among depth, width, layer types, skip connections, normalization, activation functions, and regularization. These choices influence capacity, bias-variance, interpretability, and computational cost. Data availability, latency requirements, and hardware constraints further shape decisions. Researchers evaluate architectures through ablation studies, neural architecture search, and transfer learning experiments.

Trends in modelarchitecturen include scalable, attention-based models, efficient variants of transformers, sparsity and pruning, and modular, reusable building blocks. The concept also applies beyond AI to the structural design of mathematical or computational models used to simulate real systems.