Neural Architecture Search
Neural Architecture Search (NAS) automates the design of optimal neural network architectures, aiming to replace the time-consuming and often suboptimal process of manual design. Current research focuses on improving efficiency, exploring various search algorithms (including reinforcement learning, evolutionary algorithms, and gradient-based methods), and developing effective zero-cost proxies to reduce computational demands. This field is significant because it promises to accelerate the development of high-performing models across diverse applications, from image recognition and natural language processing to resource-constrained environments like microcontrollers and in-memory computing.
Papers
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction
Ali Asghar Sharifi, Ali Zoljodi, Masoud Daneshtalab
Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning Approach
Beichen Zhang, Xiaoxing Wang, Xiaohan Qin, Junchi Yan