176 citations · 178 across the 3 of their papers we have counts for
7 papers
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
Yiyang Zhao, Yunzhuo Liu, Bo Jiang +1
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process. The proposed framework CE-NAS addresse…
Multi-Objective Neural Architecture Search by Learning Search Space Partitions
Yiyang Zhao, Linnan Wang, Tian Guo
Deploying deep learning models requires taking into consideration neural network metrics such as model size, inference latency, and #FLOPs, aside from inference accuracy. This resu…
Carbon-Efficient Neural Architecture Search
Yiyang Zhao, Tian Guo
This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The propose…
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Linnan Wang, Yiyang Zhao, Yuu Jinnai +2
Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires…
SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks
Linnan Wang, Wei Wu, Junyu Zhang +4
The performance and efficiency of distributed training of Deep Neural Networks highly depend on the performance of gradient averaging among all participating nodes, which is bounde…
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search
Linnan Wang, Yiyang Zhao, Yuu Jinnai +2
Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires…