Search Results for author:
Found 4 papers, 1 papers with code
Across-Task Neural Architecture Search via Meta Learning
In this paper, an across-task neural architecture search (AT-NAS) is proposed to address the problem through combining gradient-based meta-learning with EA-based NAS to learn over the distribution of tasks.
RepNAS: Searching for Efficient Re-parameterizing Blocks
However, it is still challenging to search for efficient networks due to the gap between the searched constraint and real inference time exists.
Effective Model Compression via Stage-wise Pruning
To overcome the unfull training, a stage-wise pruning(SWP) method is proposed, which splits a deep supernet into several stage-wise supernets to reduce the candidate number and utilize inplace distillation to supervise the stage training.
Graph Pruning for Model Compression
Different from previous work, we take the node features from a well-trained graph aggregator instead of the hand-craft features, as the states in reinforcement learning.
