Výsledky vyhledávání

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Investigating Taxi and Uber competition in New York City: Multi-agent modeling by reinforcement-learning

Autor: Vasebi, Saeed, Hayeri, Yeganeh M.

The taxi business has been overly regulated for many decades. Regulations are supposed to ensure safety and fairness within a controlled competitive environment. By influencing both drivers and riders choices and behaviors, emerging e-hailing service

Externí odkaz: http://arxiv.org/abs/2008.12530

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Mixture Representation Learning with Coupled Autoencoders

Autor: Marghi, Yeganeh M., Gala, Rohan, Sümbül, Uygar

Jointly identifying a mixture of discrete and continuous factors of variability without supervision is a key problem in unraveling complex phenomena. Variational inference has emerged as a promising method to learn interpretable mixture representatio

Externí odkaz: http://arxiv.org/abs/2007.09880

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Active recursive Bayesian inference using R\'enyi information measures

Autor: Marghi, Yeganeh M., Kocanaogullari, Aziz, Akcakaya, Murat, Erdogmus, Deniz

Recursive Bayesian inference (RBI) provides optimal Bayesian latent variable estimates in real-time settings with streaming noisy observations. Active RBI attempts to effectively select queries that lead to more informative observations to rapidly re

Externí odkaz: http://arxiv.org/abs/2004.03139

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Akademický článek

A Literature Review of Energy Optimal Adaptive Cruise Control Algorithms

Autor: Saeed Vasebi, Yeganeh M. Hayeri, Ali Mohammad Saghiri

Publikováno v: IEEE Access, Vol 11, Pp 13636-13646 (2023)

Transportation sector and its impacts on climate change have received much attention over the last decade. Energy-optimal vehicle control algorithms such as adaptive cruise control can potentially reduce fuel consumption and the consequential environ

Externí odkaz: https://doaj.org/article/c65b4230714a4b8aa1e2560789f37321