14 citations · 23 across the 6 of their papers we have counts for
9 papers
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
Yiming Chen, Yan Zhang, Bin Wang +2
Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the performanc…
Federated Stochastic Gradient Descent Begets Self-Induced Momentum
Howard H. Yang, Zuozhu Liu, Yaru Fu +2
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistic…
Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle Tracking
Gaoang Wang, Renshu Gu, Zuozhu Liu +3
Vehicle tracking is an essential task in the multi-object tracking (MOT) field. A distinct characteristic in vehicle tracking is that the trajectories of vehicles are fairly smooth…
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
Yan Zhang, Zhijiang Guo, Zhiyang Teng +4
AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph represe…
An Unsupervised Sentence Embedding Method by Mutual Information Maximization
Yan Zhang, Ruidan He, Zuozhu Liu +2
BERT is inefficient for sentence-pair tasks such as clustering or semantic search as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. Sentence…
Scheduling Policies for Federated Learning in Wireless Networks
Howard H. Yang, Zuozhu Liu, Tony Q. S. Quek +1
Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing pri…