4 citations · 9 across the 4 of their papers we have counts for
4 papers
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…
Hierarchical Bipartite Graph Convolution Networks
Marcel Nassar
Recently, graph neural networks have been adopted in a wide variety of applications ranging from relational representations to modeling irregular data domains such as point clouds…
Active Bayesian Optimization: Minimizing Minimizer Entropy
Il Memming Park, Marcel Nassar, Mijung Park
The ultimate goal of optimization is to find the minimizer of a target function.However, typical criteria for active optimization often ignore the uncertainty about the minimizer.…