neural predictor

**Neural predictor** is **a surrogate model that predicts architecture performance from structural features** - Predictors learn mapping from architecture encoding to accuracy latency or energy, enabling guided search with fewer evaluations. **What Is Neural predictor?** - **Definition**: A surrogate model that predicts architecture performance from structural features. - **Core Mechanism**: Predictors learn mapping from architecture encoding to accuracy latency or energy, enabling guided search with fewer evaluations. - **Operational Scope**: It is used in machine-learning system design to improve model quality, efficiency, and deployment reliability across complex tasks. - **Failure Modes**: Predictor extrapolation error can increase in sparsely sampled regions of search space. **Why Neural predictor Matters** - **Performance Quality**: Better methods increase accuracy, stability, and robustness across challenging workloads. - **Efficiency**: Strong algorithm choices reduce data, compute, or search cost for equivalent outcomes. - **Risk Control**: Structured optimization and diagnostics reduce unstable or misleading model behavior. - **Deployment Readiness**: Hardware and uncertainty awareness improve real-world production performance. - **Scalable Learning**: Robust workflows transfer more effectively across tasks, datasets, and environments. **How It Is Used in Practice** - **Method Selection**: Choose approach by data regime, action space, compute budget, and operational constraints. - **Calibration**: Continuously retrain predictors with active-learning sampling from uncertain candidate regions. - **Validation**: Track distributional metrics, stability indicators, and end-task outcomes across repeated evaluations. Neural predictor is **a high-value technique in advanced machine-learning system engineering** - It improves NAS sample efficiency and optimization speed.