585 citations · 784 across the 30 of their papers we have counts for
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HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis
Alireza Keshavarzian, Shahrokh Valaee
Time series classification stands as a pivotal and intricate challenge across various domains, including finance, healthcare, and industrial systems. In contemporary research, ther…
S-Rocket: Selective Random Convolution Kernels for Time Series Classification
Hojjat Salehinejad, Yang Wang, Yuanhao Yu +2
Random convolution kernel transform (Rocket) is a fast, efficient, and novel approach for time series feature extraction using a large number of independent randomly initialized 1-…
A Framework for Neural Network Pruning Using Gibbs Distributions
Alex Labach, Shahrokh Valaee
Modern deep neural networks are often too large to use in many practical scenarios. Neural network pruning is an important technique for reducing the size of such models and accele…
EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks
Hojjat Salehinejad, Shahrokh Valaee
Dropout is a well-known regularization method by sampling a sub-network from a larger deep neural network and training different sub-networks on different subsets of the data. Insp…
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…