26 citations · 29 across the 7 of their papers we have counts for
9 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),…
Training Integer-Only Deep Recurrent Neural Networks
Vahid Partovi Nia, Eyyüb Sari, Vanessa Courville +1
Recurrent neural networks (RNN) are the backbone of many text and speech applications. These architectures are typically made up of several computationally complex components such…
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…
iRNN: Integer-only Recurrent Neural Network
Eyyüb Sari, Vanessa Courville, Vahid Partovi Nia
Recurrent neural networks (RNN) are used in many real-world text and speech applications. They include complex modules such as recurrence, exponential-based activation, gate intera…
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…
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…