activity
20192022
most citedPositive-Unlabeled Compression on the Cloud

23 citations · 41 across the 6 of their papers we have counts for

collaborators

11 papers

eess.SY20226 cited

A 2030 United States Macro Grid Unlocking Geographical Diversity to Accomplish Clean Energy Goals

Yixing Xu, Daniel Olsen, Bainan Xia +4

Some U.S. states have set clean energy goals and targets in an effort to decarbonize their electricity sectors. There are many reasons for such goals and targets, including the inc…

cs.CV20214 cited

Manifold Regularized Dynamic Network Pruning

Yehui Tang, Yunhe Wang, Yixing Xu +4

Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared wit…

cs.CV2020

Kernel Based Progressive Distillation for Adder Neural Networks

Yixing Xu, Chang Xu, Xinghao Chen +3

Adder Neural Networks (ANNs) which only contain additions bring us a new way of developing deep neural networks with low energy consumption. Unfortunately, there is an accuracy dro…

cs.CV2020

Training Binary Neural Networks through Learning with Noisy Supervision

Kai Han, Yunhe Wang, Yixing Xu +3

This paper formalizes the binarization operations over neural networks from a learning perspective. In contrast to classical hand crafted rules (\eg hard thresholding) to binarize…

cs.CV2020

SCOP: Scientific Control for Reliable Neural Network Pruning

Yehui Tang, Yunhe Wang, Yixing Xu +4

This paper proposes a reliable neural network pruning algorithm by setting up a scientific control. Existing pruning methods have developed various hypotheses to approximate the im…

cs.LG20204 cited

DC-NAS: Divide-and-Conquer Neural Architecture Search

Yunhe Wang, Yixing Xu, Dacheng Tao

Most applications demand high-performance deep neural architectures costing limited resources. Neural architecture searching is a way of automatically exploring optimal deep neural…