Entropiformers

Entropiformers are a class of theoretical machine learning models that integrate entropy-driven objectives into transformer architectures. They combine information-theoretic measures of uncertainty with the expressive power of self-attention, aiming to produce representations and predictions that reflect calibrated uncertainty during learning.

Design and core ideas: An entropiformer extends the standard transformer by adding an entropy module that estimates the entropy of attention distributions or hidden representations. Entropy-aware attention constrains attention weights to a target entropy, balancing focus and diversity. Entropic regularization adds a penalty based on the output distribution’s entropy to improve calibration. Some variants adjust the entropy target dynamically across layers or training stages to reflect hierarchical uncertainty.

Training and evaluation: Entropiformers are trained with supervised, self-supervised, or reinforcement signals, combining the usual objective with entropy penalties. Evaluation considers accuracy or perplexity plus entropy-based metrics such as calibration, mutual information, and entropy of latent representations. The added entropy computations increase computational cost and implementation complexity.

Applications and status: The concept attracts interest in domains requiring calibrated uncertainty, including natural language processing, time-series forecasting, and anomaly detection. As of now, entropiformers remain a research concept with multiple informal implementations and no standardized benchmark.

See also: transformers, information theory, entropy, entropic regularization.