4 citations · 13 across the 14 of their papers we have counts for
14 papers
Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction
Cheng Liang, Teng Huang, Yi He +3
User behavior data produced during interaction with massive items in the significant data era are generally heterogeneous and sparse, leaving the recommender system (RS) a large di…
Original or Translated? On the Use of Parallel Data for Translation Quality Estimation
Baopu Qiu, Liang Ding, Di Wu +3
Machine Translation Quality Estimation (QE) is the task of evaluating translation output in the absence of human-written references. Due to the scarcity of human-labeled QE data, p…
Dual Class-Aware Contrastive Federated Semi-Supervised Learning
Qi Guo, Yong Qi, Saiyu Qi +1
Federated semi-supervised learning (FSSL), facilitates labeled clients and unlabeled clients jointly training a global model without sharing private data. Existing FSSL methods pre…
FedComm: Understanding Communication Protocols for Edge-based Federated Learning
Gary Cleland, Di Wu, Rehmat Ullah +1
Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. This exc…
An Online Sparse Streaming Feature Selection Algorithm
Feilong Chen, Di Wu, Jie Yang +1
Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applica…
DPAUC: Differentially Private AUC Computation in Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on…
Differentially Private AUC Computation in Vertical Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federa…
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data
Di Wu, Peng Zhang, Yi He +1
High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications. Latent factor analysis (LFA) is a typical representation learning method…
Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices
Di Wu, Yi He, Xin Luo
A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system. To perform hi…
Constrained Adaptive Projection with Pretrained Features for Anomaly Detection
Xingtai Gui, Di Wu, Yang Chang +1
Anomaly detection aims to separate anomalies from normal samples, and the pretrained network is promising for anomaly detection. However, adapting the pretrained features would be…
MirrorAlign: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning
Di Wu, Liang Ding, Shuo Yang +1
Word alignment is essential for the downstream cross-lingual language understanding and generation tasks. Recently, the performance of the neural word alignment models has exceeded…
Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach
Xianzhi Zhang, Yipeng Zhou, Di Wu +4
It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache…
Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning
Wenzhuo Yang, Yipeng Zhou, Maio Hu +4
Federated Learning (FL) is an emerging paradigm through which decentralized devices can collaboratively train a common model. However, a serious concern is the leakage of privacy f…
Neural Mesh Refiner for 6-DoF Pose Estimation
Di Wu, Yihao Chen, Xianbiao Qi +3
How can we effectively utilise the 2D monocular image information for recovering the 6D pose (6-DoF) of the visual objects? Deep learning has shown to be effective for robust and r…