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Found 6 papers, 2 papers with code
Are Accelerometers for Activity Recognition a Dead-end?
Overall, our work highlights the need to move away from accelerometers and calls for further exploration of using imagers for activity recognition.
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data.
Do We Need Anisotropic Graph Neural Networks?
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 #11 on
Graph Property Prediction
on ogbg-code2
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
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.
Adaptive Filters for Low-Latency and Memory-Efficient Graph Neural Networks
Scaling and deploying graph neural networks (GNNs) remains difficult due to their high memory consumption and inference latency.
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
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.
