research
∙
06/16/2023
From Hypergraph Energy Functions to Hypergraph Neural Networks
Hypergraphs are a powerful abstraction for representing higher-order int...
share
research
∙
03/13/2023
Continuous sign language recognition based on cross-resolution knowledge distillation
The goal of continuous sign language recognition(CSLR) research is to ap...
research
∙
01/18/2023
ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) mo...
research
∙
11/07/2022
Temporal superimposed crossover module for effective continuous sign language
The ultimate goal of continuous sign language recognition(CSLR) is to fa...
research
∙
07/03/2022
Continuous Sign Language Recognition via Temporal Super-Resolution Network
Aiming at the problem that the spatial-temporal hierarchical continuous ...
research
∙
06/22/2022
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Heterogeneous graph neural networks (GNNs) achieve strong performance on...
research
∙
05/21/2022
CEP3: Community Event Prediction with Neural Point Process on Graph
Many real world applications can be formulated as event forecasting on C...
research
∙
04/08/2022
Multi-scale temporal network for continuous sign language recognition
Continuous Sign Language Recognition (CSLR) is a challenging research ta...
research
∙
02/18/2022
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Heterogeneous Graph Neural Network (HGNN) has been successfully employed...
research
∙
02/01/2022
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
Recovering global rankings from pairwise comparisons is an important pro...
research
∙
03/10/2021
Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common archit...
research
∙
03/01/2021
A Biased Graph Neural Network Sampler with Near-Optimal Regret
Graph neural networks (GNN) have recently emerged as a vehicle for apply...
research
∙
10/11/2020
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
Graph neural networks (GNN) have shown great success in learning from gr...
research
∙
11/11/2019
