12 citations · 17 across the 5 of their papers we have counts for
5 papers
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…
p-Meta: Towards On-device Deep Model Adaptation
Zhongnan Qu, Zimu Zhou, Yongxin Tong +1
Data collected by IoT devices are often private and have a large diversity across users. Therefore, learning requires pre-training a model with available representative data sample…
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…