Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for training and inference.
Scaling and deploying graph neural networks (GNNs) remains difficult due to their high memory consumption and inference latency.
In recent years graph neural network (GNN)-based approaches have become a popular strategy for processing point cloud data, regularly achieving state-of-the-art performance on a variety of tasks.
We demonstrate that EGC outperforms existing approaches across 6 large and diverse benchmark datasets, and conclude by discussing questions that our work raise for the community going forward.
Ranked #7 on Graph Property Prediction on ogbg-code2
Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data.
Overall, our work highlights the need to move away from accelerometers and calls for further exploration of using imagers for activity recognition.