585 citations · 841 across the 29 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…
Opposition based Ensemble Micro Differential Evolution
Hojjat Salehinejad, Shahryar Rahnamayan, Hamid R. Tizhoosh
Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE). A small population size decreases the computational complexity but also reduces the exp…
Learning Over Long Time Lags
Hojjat Salehinejad
The advantage of recurrent neural networks (RNNs) in learning dependencies between time-series data has distinguished RNNs from other deep learning models. Recently, many advances…
Diversity Enhancement for Micro-Differential Evolution
Hojjat Salehinejad, Shahryar Rahnamayan, Hamid R. Tizhoosh
The differential evolution (DE) algorithm suffers from high computational time due to slow nature of evaluation. In contrast, micro-DE (MDE) algorithms employ a very small populati…
Deep Recurrent Neural Networks for Sequential Phenotype Prediction in Genomics
Farhad Pouladi, Hojjat Salehinejad, Amir Mohammad Gilani
In analyzing of modern biological data, we are often dealing with ill-posed problems and missing data, mostly due to high dimensionality and multicollinearity of the dataset. In th…
Combined A*-Ants Algorithm: A New Multi-Parameter Vehicle Navigation Scheme
Hojjat Salehinejad, Hossein Nezamabadi-pour, Saeid Saryazdi +1
In this paper a multi-parameter A*(A- star)-ants based algorithm is proposed in order to find the best optimized multi-parameter path between two desired points in regions. This al…
Toward Smart Power Grids: Communication Network Design for Power Grids Synchronization
Hojjat Salehinejad, Farhad Pouladi, Siamak Talebi
In smart power grids, keeping the synchronicity of generators and the corresponding controls is of great importance. To do so, a simple model is employed in terms of swing equation…