22 citations · 34 across the 7 of their papers we have counts for
7 papers
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
Kourosh Hakhamaneshi, Marcel Nassar, Mariano Phielipp +2
Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supe…
Connection Management xAPP for O-RAN RIC: A Graph Neural Network and Reinforcement Learning Approach
Oner Orhan, Vasuki Narasimha Swamy, Thomas Tetzlaff +3
Connection management is an important problem for any wireless network to ensure smooth and well-balanced operation throughout. Traditional methods for connection management (speci…
On Local Aggregation in Heterophilic Graphs
Hesham Mostafa, Marcel Nassar, Somdeb Majumdar
Many recent works have studied the performance of Graph Neural Networks (GNNs) in the context of graph homophily - a label-dependent measure of connectivity. Traditional GNNs gener…
Structured Citation Trend Prediction Using Graph Neural Networks
Daniel Cummings, Marcel Nassar
Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their co…
Permutohedral-GCN: Graph Convolutional Networks with Global Attention
Hesham Mostafa, Marcel Nassar
Graph convolutional networks (GCNs) update a node's feature vector by aggregating features from its neighbors in the graph. This ignores potentially useful contributions from dista…
Conditional Graph Neural Processes: A Functional Autoencoder Approach
Marcel Nassar, Xin Wang, Evren Tumer
We introduce a novel encoder-decoder architecture to embed functional processes into latent vector spaces. This embedding can then be decoded to sample the encoded functions over a…