Machines that design
other machines.
96.4% accuracy. 14 runs.
Your team needed 318 and got 94.1%.
Across 12 benchmark tasks, Hyperparameter AutoNAS consistently outperforms hand-tuned architectures while using 95.6% fewer experiment iterations.
Benchmark conducted on ImageNet-subset (100K samples), CIFAR-100, and Penn Treebank. All methods were given identical compute budgets (GPU-hours). Hyperparameter AutoNAS uses a differentiable architecture search (DARTS-variant) combined with multi-fidelity Bayesian optimization. Results averaged across 5 independent seeds. Confidence intervals at 95%. Hardware: 8× NVIDIA A100 80GB. Evaluation period: 2025-Q3 through 2026-Q1.
7 of 7 metrics.
No category where we don't win outright.
Evaluated against three anonymized commercial AutoML platforms and an expert hand-tuning baseline. Cyan cells indicate categories where Hyperparameter leads.
Novel architectures your team
wouldn't have tried.
AutoNAS doesn't search the same design space human researchers explore. It finds architectures that break conventional intuition — and consistently validates them.
$34K/year.
Manual tuning costs 14.8× more.
Total cost of ownership including compute, tooling, and researcher time. Hyperparameter replaces the experiment backlog that consumes your team.
Download the complete methodology
48 pages. Every benchmark, every architecture, every cost breakdown. Reproducible experiments with full source code and seed logs.
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