7 citations · 13 across the 6 of their papers we have counts for
6 papers
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning
Shanglin Zhou, Mikhail A. Bragin, Lynn Pepin +3
Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline significantly increases the…
Towards Real-Time Temporal Graph Learning
Deniz Gurevin, Mohsin Shan, Tong Geng +3
In recent years, graph representation learning has gained significant popularity, which aims to generate node embeddings that capture features of graphs. One of the methods to achi…
Towards Sparsification of Graph Neural Networks
Hongwu Peng, Deniz Gurevin, Shaoyi Huang +4
As real-world graphs expand in size, larger GNN models with billions of parameters are deployed. High parameter count in such models makes training and inference on graphs expensiv…
Secure Remote Attestation with Strong Key Insulation Guarantees
Deniz Gurevin, Chenglu Jin, Phuong Ha Nguyen +2
Recent years have witnessed a trend of secure processor design in both academia and industry. Secure processors with hardware-enforced isolation can be a solid foundation of cloud…
Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation
Deniz Gurevin, Shanglin Zhou, Lynn Pepin +4
Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i.e., training, pruning an…
Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples
Kaleel Mahmood, Deniz Gurevin, Marten van Dijk +1
Many defenses have recently been proposed at venues like NIPS, ICML, ICLR and CVPR. These defenses are mainly focused on mitigating white-box attacks. They do not properly examine…