149 citations · 295 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…
Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters
Hesham Mostafa
Federated learning is a distributed, privacy-aware learning scenario which trains a single model on data belonging to several clients. Each client trains a local model on its data…
Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization
Hesham Mostafa, Xin Wang
Modern deep neural networks are typically highly overparameterized. Pruning techniques are able to remove a significant fraction of network parameters with little loss in accuracy.…
Surrogate Gradient Learning in Spiking Neural Networks
Emre O. Neftci, Hesham Mostafa, Friedemann Zenke
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate these benefits into hardware, a growing number of neu…