neural-arch-taxonomy-mlp-cnn-rnn-transformer-mamba

IN premise — entries/2026/06/21/wiki-Deep_learning-chunk-7.md

Created 2026-06-21T09:55:49+00:00

Neural network architecture taxonomy progresses: MLP (feedforward) → CNN (spatial) → RNN/LSTM/GRU (sequential) → Transformer (attention-based, now dominant) → Mamba (state-space, emerging alternative).

Dependents

These beliefs depend on this one:

• IN architecture-taxonomy-recapitulates-data-structure-hierarchy — The neural network architecture taxonomy (MLP → CNN → RNN/LSTM → Transformer → Mamba) mirrors a hierarchy of data structure assumptions — MLPs assume no structure, CNNs assume local spatial structure, RNNs assume sequential structure, Transformers assume global relational structure, and each architecture's effectiveness is explained by matching its inductive bias to the manifold geometry of its target data type.