2 citations · 2 across the 3 of their papers we have counts for
5 papers
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…
Batch Normalization in Quantized Networks
Eyyüb Sari, Vahid Partovi Nia
Implementation of quantized neural networks on computing hardware leads to considerable speed up and memory saving. However, quantized deep networks are difficult to train and batc…
How Does Batch Normalization Help Binary Training?
Eyyüb Sari, Mouloud Belbahri, Vahid Partovi Nia
Binary Neural Networks (BNNs) are difficult to train, and suffer from drop of accuracy. It appears in practice that BNNs fail to train in the absence of Batch Normalization (BatchN…
Differentiable Mask for Pruning Convolutional and Recurrent Networks
Ramchalam Kinattinkara Ramakrishnan, Eyyüb Sari, Vahid Partovi Nia
Pruning is one of the most effective model reduction techniques. Deep networks require massive computation and such models need to be compressed to bring them on edge devices. Most…
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks
Mouloud Belbahri, Eyyüb Sari, Sajad Darabi +1
Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their…