149 citations · 301 across the 6 of their papers we have counts for
7 papers
Attention-based Image Upsampling
Souvik Kundu, Hesham Mostafa, Sharath Nittur Sridhar +1
Convolutional layers are an integral part of many deep neural network solutions in computer vision. Recent work shows that replacing the standard convolution operation with mechani…
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…
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems
Mark D. McDonnell, Hesham Mostafa, Runchun Wang +1
Batch-normalization (BN) layers are thought to be an integrally important layer type in today's state-of-the-art deep convolutional neural networks for computer vision tasks such a…
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…