74 citations · 149 across the 12 of their papers we have counts for
12 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…
Federated Unsupervised Representation Learning
Fengda Zhang, Kun Kuang, Zhaoyang You +6
To leverage enormous unlabeled data on distributed edge devices, we formulate a new problem in federated learning called Federated Unsupervised Representation Learning (FURL) to le…
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…
Distilled One-Shot Federated Learning
Yanlin Zhou, George Pu, Xiyao Ma +2
Current federated learning algorithms take tens of communication rounds transmitting unwieldy model weights under ideal circumstances and hundreds when data is poorly distributed.…
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…