9 citations · 12 across the 5 of their papers we have counts for
5 papers
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration
Ran Liu, Sahil Khose, Jingyun Xiao +4
Despite significant advances in deep learning, models often struggle to generalize well to new, unseen domains, especially when training data is limited. To address this challenge,…
Half-Hop: A graph upsampling approach for slowing down message passing
Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor +6
Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail whe…
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
Jorge Quesada, Lakshmi Sathidevi, Ran Liu +10
There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the i…
FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference
Rishov Sarkar, Stefan Abi-Karam, Yuqi He +2
Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to graph-related problems such as quantum chemistry, drug discovery, and high…
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
Stefan Abi-Karam, Yuqi He, Rishov Sarkar +3
Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to ubiquitous graph-related problems such as quantum chemistry, drug discovery…