26 citations · 29 across the 7 of their papers we have counts for
4 papers · 1 filter
Adaptive Binary-Ternary Quantization
Ryan Razani, Grégoire Morin, Vahid Partovi Nia +1
Neural network models are resource hungry. It is difficult to deploy such deep networks on devices with limited resources, like smart wearables, cellphones, drones, and autonomous…
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…