Architecture Search

Summary

Architecture Search is an emerging field in machine learning that aims to automate the design of neural network architectures. Recent approaches have made significant progress in this area, employing various techniques to efficiently explore the vast search space of possible architectures. Differentiable Architecture Search (DARTS) introduces a continuous relaxation of the architecture representation, enabling gradient-based optimization. Reinforcement learning has been successfully applied to generate high-performing architectures for image classification and language modeling tasks. More ambitious approaches, such as AutoML-Zero, evolve machine learning algorithms from basic mathematical operations, while Generative Teaching Networks accelerate the evaluation of candidate architectures by generating synthetic training data. Resource-Efficient Neural Architect (RENA) incorporates computational resource constraints into the search process. These methods have demonstrated the ability to discover architectures that rival or surpass human-designed models, while significantly reducing the time and effort required for architecture design.

Research Papers

• DARTS Differentiable Architecture Search
• Neural Architecture Search with Reinforcement Learning
• AutoML-Zero Evolving Machine Learning Algorithms From Scratch
• Generative Teaching Networks Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
• Resource-Efficient Neural Architect