Search Results for author:
Found 7 papers, 3 papers with code
GIPA: General Information Propagation Algorithm for Graph Learning
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Accurate Fine-grained Layout Analysis for the Historical Tibetan Document Based on the Instance Segmentation
The experimental results show that the proposed method delivers a decent 72. 7% average precision on our dataset.
A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting
A new variational mode decomposition (VMD) based deep learning approach is proposed in this paper for time series forecasting problem.
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
Herein, we present GraphTheta, the first distributed and scalable graph learning system built upon vertex-centric distributed graph processing with neural network operators implemented as user-defined functions.
MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection
Fusing the two kinds of data usually helps to improve the detection results.
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving
In this work, we propose a novel framework PCDF(Parallel-Computing Distributed Framework), allowing to split the computation cost into three parts and to deploy them in the pre-module in parallel with the retrieval stage, the middle-module for ranking ads, and the post-module for re-ranking ads with external items.
Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation
The next step is to map the contact points of the retrieved objects to the new object.
