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Found 8 papers, 2 papers with code
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams
Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates.
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective
Many Graph Neural Network (GNN) training systems have emerged recently to support efficient GNN training.
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments
After extensive experiments and analysis, we find that existing task orchestrating methods fail to fully utilize the heterogeneous resources, limited by inefficient CPU processing or GPU resource contention.
RI-LIO: Reflectivity Image Assisted Tightly-Coupled LiDAR-Inertial Odometry
The searched nearest neighbor point is used to render a sparse reflectivity image after LiDAR motion distortion information is given by its corresponding raw point.
MVLayoutNet:3D layout reconstruction with multi-view panoramas
We jointly train a layout module to produce an initial layout and a novel MVS module to obtain accurate layout geometry.
PrimitiveNet: Primitive Instance Segmentation With Local Primitive Embedding Under Adversarial Metric
We present PrimitiveNet, a novel approach for high-resolution primitive instance segmentation from point clouds on a large scale.
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning
This motivates us to use a multiplex structure in a diverse way and utilize a priori properties of graphs to guide the learning.
GANCoder: An Automatic Natural Language-to-Programming Language Translation Approach based on GAN
We propose GANCoder, an automatic programming approach based on Generative Adversarial Networks (GAN), which can generate the same functional and logical programming language codes conditioned on the given natural language utterances.
