4 citations · 10 across the 4 of their papers we have counts for
4 papers
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization
Yanyu Li, Pu Zhao, Geng Yuan +3
Neural architecture search (NAS) and network pruning are widely studied efficient AI techniques, but not yet perfect. NAS performs exhaustive candidate architecture search, incurri…
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search
Minbin Huang, Zhijian Huang, Changlin Li +4
Neural Architecture Search (NAS) aims to find efficient models for multiple tasks. Beyond seeking solutions for a single task, there are surging interests in transferring network d…
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search
Yawen Duan, Xin Chen, Hang Xu +4
Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existin…
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search
Xin Chen, Yawen Duan, Zewei Chen +5
Neural Architecture Search (NAS) achieved many breakthroughs in recent years. In spite of its remarkable progress, many algorithms are restricted to particular search spaces. They…