DARTS: Differentiable Architecture Search

Key takeaways for engineers/specialists: DARTS introduces a continuous relaxation approach to architecture search, leveraging gradient descent for efficient optimization. It achieves state-of-the-art results on image classification and language modeling tasks with significantly less computational cost. Challenges include the gap between continuous and discrete architecture representation, computational cost of second-order approximation, and sensitivity to hyperparameters.

Read full paper: https://arxiv.org/abs/1806.09055

Tags: Deep Learning, Optimization, Machine Learning