Neural Architecture Search

Definitions

1. Neural Architecture Search is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural architectures and deciding where capacity should live, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to parameter efficiency, expressiveness, and hardware fit, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.

   In plain English: Neural Architecture Search is an AI concept teams use to train models, guide predictions, or make model behavior more reliable and easier to control in practice.

   Example: "We evaluated Neural Architecture Search in the new model pipeline because the baseline was plateauing; once it was wired into training and evaluation, quality improved enough to justify rolling it into the next release."

Related Terms

• Lemmatization
• Linear Layer
• Linear Probe
• Linear Regression
• Llama
• Logistic Regression
• Logit
• Long Short-Term Memory