1.5k citations · 3.2k across the 75 of their papers we have counts for
6 papers · 1 filter
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring
Zhengquan Luo, Yunlong Wang, Zilei Wang +2
Attributes skew hinders the current federated learning (FL) frameworks from consistent optimization directions among the clients, which inevitably leads to performance reduction an…
Graph Classification by Mixture of Diverse Experts
Fenyu Hu, Liping Wang, Shu Wu +2
Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imb…
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Fenyu Hu, Yanqiao Zhu, Shu Wu +3
Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convoluti…
Fast Supervised Discrete Hashing
Jie Gui, Tongliang Liu, Zhenan Sun +2
Learning-based hashing algorithms are ``hot topics" because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based ha…
Supervised Discrete Hashing with Relaxation
Jie Gui, Tongliang Liu, Zhenan Sun +2
Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data such as documents, images, and videos.…
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
Huaibo Huang, Zhihang Li, Ran He +2
We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of self-evaluating the quality o…