8 citations · 24 across the 7 of their papers we have counts for
3 papers · 1 filter
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
Lorenz Kummer, Samir Moustafa, Nils N. Kriege +1
Prior attacks on graph neural networks have mostly focused on graph poisoning and evasion, neglecting the network's weights and biases. Traditional weight-based fault injection att…
On the Two Sides of Redundancy in Graph Neural Networks
Franka Bause, Samir Moustafa, Johannes Langguth +2
Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes. With increasing depth, information from more distant nodes i…
Adaptive Precision Training (AdaPT): A dynamic fixed point quantized training approach for DNNs
Lorenz Kummer, Kevin Sidak, Tabea Reichmann +1
Quantization is a technique for reducing deep neural networks (DNNs) training and inference times, which is crucial for training in resource constrained environments or application…