41 citations · 115 across the 6 of their papers we have counts for
6 papers
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
Yuanyuan Chen, Zichen Chen, Pengcheng Wu +1
Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose sign…
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
Yuanyuan Chen, Boyang Li, Han Yu +2
The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HYDRA, which in…
Face Detection using Deep Learning: An Improved Faster RCNN Approach
Xudong Sun, Pengcheng Wu, Steven C. H. Hoi
In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark eva…
HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search
Ji Wan, Sheng Tang, Yongdong Zhang +3
Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high di…
A Framework of Sparse Online Learning and Its Applications
Dayong Wang, Pengcheng Wu, Peilin Zhao +1
The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including hi…
Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning
Peilin Zhao, Jialei Wang, Pengcheng Wu +2
Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded numb…