scientific-neural-architecture-search

Neural architecture search can be automated using DARTS-style differentiable search combined with Optuna-based Pareto optimization to balance accuracy against efficiency metrics like FLOPs and parameters. Define a custom search space and configure the objective function to guide the automated exploration.

Differentiable NAS relaxes the discrete architecture search space into a continuous one, enabling gradient-based optimization. DARTS enables efficient deep learning model design by allowing the search process to optimize architectural weights and network parameters simultaneously during training.

Yes, you can use Optuna for Pareto optimization to explore deep learning architectures by defining an objective function that evaluates multiple metrics simultaneously. This identifies a Pareto front representing the best trade-offs between model accuracy and computational efficiency.

Define a custom search space for neural architecture search by specifying the candidate operations and network connections available. This configurable space forms the foundation for DARTS and Optuna search algorithms to explore and evaluate architectural variations against your objective function.

Yes, this neural architecture search workflow supports multiple evaluation budgets to assess efficiency versus accuracy in real-world models. You can configure the objective function and search constraints to find optimal architectures within your specific computational limits and resource constraints.

After completing the Optuna-guided search, the NAS workflow provides the ability to export the best architectures and the resulting Pareto fronts. This allows you to extract the optimized model designs and analyze the trade-off curves for downstream deployment.