85 citations · 226 across the 15 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
A Semi-Supervised Assessor of Neural Architectures
Yehui Tang, Yunhe Wang, Yixing Xu +6
Neural architecture search (NAS) aims to automatically design deep neural networks of satisfactory performance. Wherein, architecture performance predictor is critical to efficient…
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks
Yehui Tang, Yunhe Wang, Yixing Xu +4
Deep neural networks often consist of a great number of trainable parameters for extracting powerful features from given datasets. On one hand, massive trainable parameters signifi…
U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies
Yixing Xu, Nathan Myhrvold, Dhileep Sivam +9
Planning for power systems with high penetrations of variable renewable energy requires higher spatial and temporal granularity. However, most publicly available test systems are o…