4 citations · 17 across the 7 of their papers we have counts for
9 papers
Server Averaging for Federated Learning
George Pu, Yanlin Zhou, Dapeng Wu +1
Federated learning allows distributed devices to collectively train a model without sharing or disclosing the local dataset with a central server. The global model is optimized by…
Asking Complex Questions with Multi-hop Answer-focused Reasoning
Xiyao Ma, Qile Zhu, Yanlin Zhou +2
Asking questions from natural language text has attracted increasing attention recently, and several schemes have been proposed with promising results by asking the right question…
PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders
Yanjun Li, Shujian Yu, Jose C. Principe +2
Although substantial efforts have been made to learn disentangled representations under the variational autoencoder (VAE) framework, the fundamental properties to the dynamics of l…
A Batch Normalized Inference Network Keeps the KL Vanishing Away
Qile Zhu, Jianlin Su, Wei Bi +4
Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep…
Arbitrary-sized Image Training and Residual Kernel Learning: Towards Image Fraud Identification
Hongyu Li, Xiaogang Huang, Zhihui Fu +1
Preserving original noise residuals in images are critical to image fraud identification. Since the resizing operation during deep learning will damage the microstructures of image…
Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework
Hongyu Li, Dan Meng, Hong Wang +1
With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making…