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Found 5 papers, 5 papers with code
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration
To address this challenge, we propose a novel approach for distribution-aware latent augmentation that leverages the relationships across samples to guide the augmentation procedure.
Half-Hop: A graph upsampling approach for slowing down message passing
Message passing neural networks have shown a lot of success on graph-structured data.
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MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image.
FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference
First, we propose a novel and scalable dataflow architecture, which generally supports a wide range of GNN models with message-passing mechanism.
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
Second, we aim to support a diverse set of GNN models with the extensibility to flexibly adapt to new models.
