Automated discovery of optimal neural network architectures
An efficient neural architecture search framework that automatically discovers high-performing network architectures for computer vision tasks with minimal human intervention.
Designing neural network architectures requires extensive expertise and countless hours of experimentation. The search space is vast, and manual exploration is inefficient.
Implemented differentiable architecture search (DARTS) with progressive space pruning for efficiency. Added multi-objective optimization to balance accuracy, latency, and parameter count. Developed a meta-learning warm-start strategy that transfers knowledge across datasets.
Found architectures achieving ImageNet top-1 accuracy competitive with hand-designed networks while using 40% fewer parameters. Search process completes in under 1 GPU-day compared to weeks for traditional NAS.