15 citations · 43 across the 8 of their papers we have counts for
8 papers
MixBCT: Towards Self-Adapting Backward-Compatible Training
Yu Liang, Yufeng Zhang, Shiliang Zhang +4
Backward-compatible training circumvents the need for expensive updates to the old gallery database when deploying an advanced new model in the retrieval system. Previous methods a…
Geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure
Xiao Jiang, Zean Tian, Kenli Li
Understanding the dynamic processes of the glassy system continues to be challenging. Recent advances have shown the power of graph neural networks (GNNs) for determining the corre…
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning
Jianguo Chen, Kenli Li, Keqin Li +2
As a new generation of Public Bicycle-sharing Systems (PBS), the dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to…
Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network
Jianguo Chen, Kenli Li, Keqin Li +2
Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, red…
SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition
Hao Li, Zixuan Li, Kenli Li +3
Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor matrices to obtain an optimal low-rank representation feature for the \underline{H}igh-\under…
EHAP-ORAM: Efficient Hardware-Assisted Persistent ORAM System for Non-volatile Memory
Gang Liu, Kenli Li, Zheng Xiao +1
Oblivious RAM (ORAM) is a provable secure primitive to prevent access pattern leakage on the memory bus. It serves as the intermediate layer between the trusted on-chip components…
Semi-Disentangled Representation Learning in Recommendation System
Weiguang Chen, Wenjun Jiang, Xueqi Li +3
Disentangled representation has been widely explored in many fields due to its maximal compactness, interpretability and versatility. Recommendation system also needs disentangleme…
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models
Yufeng Zhang, Jialu Pan, Wanwei Liu +5
Recent research has revealed that deep generative models including flow-based models and Variational Autoencoders may assign higher likelihoods to out-of-distribution (OOD) data th…