68 citations · 230 across the 38 of their papers we have counts for
6 papers · 1 filter
PLD: A Choice-Theoretic List-Wise Knowledge Distillation
Ejafa Bassam, Dawei Zhu, Kaigui Bian
Knowledge distillation is a model compression technique in which a compact "student" network is trained to replicate the predictive behavior of a larger "teacher" network. In logit…
TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting
Xu Chen, Yuanxing Zhang, Lun Du +4
Traffic flow forecasting is of great significance for improving the efficiency of transportation systems and preventing emergencies. Due to the highly non-linearity and intricate e…
Differentiable Feature Aggregation Search for Knowledge Distillation
Yushuo Guan, Pengyu Zhao, Bingxuan Wang +4
Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distri…
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Chengxu Yang, Qipeng Wang, Mengwei Xu +4
Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is h…
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System
Pengyu Zhao, Kecheng Xiao, Yuanxing Zhang +2
Recently, deep learning models have been widely spread in the industrial recommender systems and boosted the recommendation quality. Though having achieved remarkable success, the…
Federated Neural Architecture Search
Jinliang Yuan, Mengwei Xu, Yuxin Zhao +4
To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data. However, training over decentralized…