585 citations · 784 across the 30 of their papers we have counts for
4 papers · 1 filter
Regularizing Neural Networks by Stochastically Training Layer Ensembles
Alex Labach, Shahrokh Valaee
Dropout and similar stochastic neural network regularization methods are often interpreted as implicitly averaging over a large ensemble of models. We propose STE (stochastically t…
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…
Decimeter Ranging with Channel State Information
Navid Tadayon, Muhammed T. Rahman, Shuo Han +2
This paper aims at the problem of time-of-flight (ToF) estimation using channel state information (CSI) obtainable from commercialized MIMO-OFDM WLAN receivers. It was often claime…
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…