V
Vadim Sokolov
Researcher at George Mason University
Publications - 70
Citations - 1906
Vadim Sokolov is an academic researcher from George Mason University. The author has contributed to research in topics: Deep learning & Stochastic gradient descent. The author has an hindex of 15, co-authored 60 publications receiving 1349 citations. Previous affiliations of Vadim Sokolov include Argonne National Laboratory & Northern Illinois University.
Papers
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Deep learning for short-term traffic flow prediction
Nicholas G. Polson,Vadim Sokolov +1 more
TL;DR: A deep learning model is developed that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers to predict traffic flows and identifies spatio-temporal relations among predictors and other layers model nonlinear relations.
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POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations
TL;DR: In this article, the authors present an agent-based modeling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations.
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Deep Learning: A Bayesian Perspective
Nicholas G. Polson,Vadim Sokolov +1 more
TL;DR: By taking a Bayesian probabilistic perspective, this work provides a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning.
POLARIS: Agent-Based Modeling Framework Development and Implementation for Integrated Travel Demand and Network and Operations Simulations
TL;DR: This paper discusses the development of an agent-based modeling software development kit, and the implementation and validation of a model that integrates dynamic simulation of travel demand, network supply and network operations, built from the POLARIS framework.
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Analysis of the effects of connected-automated vehicle technologies on travel demand
TL;DR: In this article, the authors analyzed the potential effects of CAV technologies from a systems perspective, focusing on gains and losses to an individual vehicle, at a single intersegment.