Model Optimization

2 articles

Artificial Intelligence 12 Sep 2026 6 min read

Exit Transformer Classifiers Early with Entropy Thresholds

A transformer classifier normally sends every input through every layer, even when an intermediate representation already supports a concentrated class prediction. Entropy-based early exit changes that fixed-depth behavior. Prediction heads attached to intermediate layers estimate class distributions, and inference can stop once a distribution passes a configured entropy threshold. The mechanism makes model depth input-dependent. Some inputs may leave after relatively few layers, while uncertain inputs continue through more of the network. That flexibility also introduces a new source of error: an intermediate head can be confident and still be wrong.

Artificial Intelligence Transformers

Artificial Intelligence 03 Sep 2026 9 min read

Knowledge Distillation for Smaller AI Models

A large model may produce useful predictions but still be too expensive or slow for the environment where it must run. A mobile application, an edge device, or a high-volume service can have tighter limits on memory, latency, and compute. Knowledge distillation is one way to address that gap. Instead of training a smaller model only from the original labels, we also train it to imitate information produced by a stronger teacher model. The smaller model is called the student.

Artificial Intelligence Machine Learning