37 citations · 78 across the 7 of their papers we have counts for
9 papers
Fed2: Feature-Aligned Federated Learning
Fuxun Yu, Weishan Zhang, Zhuwei Qin +5
Federated learning learns from scattered data by fusing collaborative models from local nodes. However, the conventional coordinate-based model averaging by FedAvg ignored the rand…
Third ArchEdge Workshop: Exploring the Design Space of Efficient Deep Neural Networks
Fuxun Yu, Dimitrios Stamoulis, Di Wang +2
This paper gives an overview of our ongoing work on the design space exploration of efficient deep neural networks (DNNs). Specifically, we cover two aspects: (1) static architectu…
AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency
Fuxun Yu, Chenchen Liu, Di Wang +2
Convolutional Neural Networks (CNNs) achieved great cognitive performance at the expense of considerable computation load. To relieve the computation load, many optimization works…
Heterogeneous Federated Learning
Fuxun Yu, Weishan Zhang, Zhuwei Qin +5
Federated learning learns from scattered data by fusing collaborative models from local nodes. However, due to chaotic information distribution, the model fusion may suffer from st…
Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning
Fuxun Yu, Di Wang, Yinpeng Chen +7
Current state-of-the-art object detectors can have significant performance drop when deployed in the wild due to domain gaps with training data. Unsupervised Domain Adaptation (UDA…
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization
Dimitrios Stamoulis, Ruizhou Ding, Di Wang +4
Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardw…