32 citations · 41 across the 7 of their papers we have counts for
7 papers
Minimalist Traffic Prediction: Linear Layer Is All You Need
Wenying Duan, Hong Rao, Wei Huang +1
Traffic prediction is essential for the progression of Intelligent Transportation Systems (ITS) and the vision of smart cities. While Spatial-Temporal Graph Neural Networks (STGNNs…
MIMONet: Multi-Input Multi-Output On-Device Deep Learning
Zexin Li, Xiaoxi He, Yufei Li +3
Future intelligent robots are expected to process multiple inputs simultaneously (such as image and audio data) and generate multiple outputs accordingly (such as gender and emotio…
Localised Adaptive Spatial-Temporal Graph Neural Network
Wenying Duan, Xiaoxi He, Zimu Zhou +2
Spatial-temporal graph models are prevailing for abstracting and modelling spatial and temporal dependencies. In this work, we ask the following question: whether and to what exten…
Subspace-Configurable Networks
Dong Wang, Olga Saukh, Xiaoxi He +1
While the deployment of deep learning models on edge devices is increasing, these models often lack robustness when faced with dynamic changes in sensed data. This can be attribute…
Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks
Wenying Duan, Xiaoxi He, Lu Zhou +2
Time series forecasting has widespread applications in urban life ranging from air quality monitoring to traffic analysis. However, accurate time series forecasting is challenging…
Pruning-Aware Merging for Efficient Multitask Inference
Xiaoxi He, Dawei Gao, Zimu Zhou +2
Many mobile applications demand selective execution of multiple correlated deep learning inference tasks on resource-constrained platforms. Given a set of deep neural networks, eac…
Multi-Task Zipping via Layer-wise Neuron Sharing
Xiaoxi He, Zimu Zhou, Lothar Thiele
Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks on-device. Yet the complexity…