11 citations · 16 across the 5 of their papers we have counts for
5 papers
Efficient Training Under Limited Resources
Mahdi Zolnouri, Dounia Lakhmiri, Christophe Tribes +2
Training time budget and size of the dataset are among the factors affecting the performance of a Deep Neural Network (DNN). This paper shows that Neural Architecture Search (NAS),…
Scaling Deep Networks with the Mesh Adaptive Direct Search algorithm
Dounia Lakhmiri, Mahdi Zolnouri, Vahid Partovi Nia +2
Deep neural networks are getting larger. Their implementation on edge and IoT devices becomes more challenging and moved the community to design lighter versions with similar perfo…
Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks
Vahid Partovi Nia, Alireza Ghaffari, Mahdi Zolnouri +1
Performance optimization of deep learning models is conducted either manually or through automatic architecture search, or a combination of both. On the other hand, their performan…
Demystifying and Generalizing BinaryConnect
Tim Dockhorn, Yaoliang Yu, Eyyüb Sari +2
BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization. However, our understanding of the inner workings of BC is still quite…
Importance of Data Loading Pipeline in Training Deep Neural Networks
Mahdi Zolnouri, Xinlin Li, Vahid Partovi Nia
Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loading data takes a signifi…