18 citations · 33 across the 6 of their papers we have counts for
6 papers
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…
A Practical Layer-Parallel Training Algorithm for Residual Networks
Qi Sun, Hexin Dong, Zewei Chen +5
Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which a…
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…
New Interpretations of Normalization Methods in Deep Learning
Jiacheng Sun, Xiangyong Cao, Hanwen Liang +3
In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalizati…
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient
Zewei Chen, Fengwei Zhou, George Trimponias +1
Multi-objective Neural Architecture Search (NAS) aims to discover novel architectures in the presence of multiple conflicting objectives. Despite recent progress, the problem of ap…
MANAS: Multi-Agent Neural Architecture Search
Vasco Lopes, Fabio Maria Carlucci, Pedro M Esperança +6
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a g…