585 citations · 784 across the 30 of their papers we have counts for
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A Framework For Pruning Deep Neural Networks Using Energy-Based Models
Hojjat Salehinejad, Shahrokh Valaee
A typical deep neural network (DNN) has a large number of trainable parameters. Choosing a network with proper capacity is challenging and generally a larger network with excessive…
Pruning of Convolutional Neural Networks Using Ising Energy Model
Hojjat Salehinejad, Shahrokh Valaee
Pruning is one of the major methods to compress deep neural networks. In this paper, we propose an Ising energy model within an optimization framework for pruning convolutional ker…
Survey of Dropout Methods for Deep Neural Networks
Alex Labach, Hojjat Salehinejad, Shahrokh Valaee
Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice…
Ising-Dropout: A Regularization Method for Training and Compression of Deep Neural Networks
Hojjat Salehinejad, Shahrokh Valaee
Overfitting is a major problem in training machine learning models, specifically deep neural networks. This problem may be caused by imbalanced datasets and initialization of the m…
Recent Advances in Recurrent Neural Networks
Hojjat Salehinejad, Sharan Sankar, Joseph Barfett +2
Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where a…