musterrecurrent

Musterrecurrent is a term used in theoretical computer science and machine learning to describe a class of sequential models that integrate motif detection with recurrent computation to capture repeating patterns in data. The word combines muster, derived from the German for pattern or specimen, with recurrent to reflect the ongoing processing of information over time.

Concept and architecture: The model maintains a motif dictionary of recurring subsequences or motifs. At each step, it scans the input for potential motifs, computes a motif usage vector, and combines it with the standard hidden state through a gated fusion mechanism. This yields a state that encodes both the current context and remembered patterns. In practice, musterrecurrent can be implemented as an extension of traditional recurrent networks (such as RNNs or LSTMs) with an additional motif attention or routing component that prioritizes recognized patterns.

Training and variants: Training can proceed with backpropagation through time, optionally with pretraining for motif discovery. Variants may learn the motif dictionary end-to-end or rely on a fixed dictionary. The approach adds computational complexity proportional to the number and length of motifs, and practitioners commonly apply regularization to prevent overfitting to spurious patterns while encouraging stable motif usage.

Applications and evaluation: Musterrecurrent has been proposed for tasks involving data with recurring structure, including time-series forecasting, anomaly detection, motif discovery in genomic sequences, and music or audio signal modeling. Evaluations typically compare musterrecurrent models to standard RNNs, noting improvements when the data exhibit salient repeating patterns.

See also: recurrent neural networks, motif discovery, motif-based models.