1 code implementation • 22 Dec 2023 • Shane Bergsma, Timothy Zeyl, Javad Rahimipour Anaraki, Lei Guo
We present coarse-to-fine autoregressive networks (C2FAR), a method for modeling the probability distribution of univariate, numeric random variables.
2 code implementations • 26 Feb 2019 • Javad Rahimipour Anaraki, Hamid Usefi
Consider a supervised dataset $D=[A\mid \textbf{b}]$, where $\textbf{b}$ is the outcome column, rows of $D$ correspond to observations, and columns of $A$ are the features of the dataset.
no code implementations • 31 Jul 2018 • Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn
This paper presents a new feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset.
no code implementations • 17 Aug 2020 • Javad Rahimipour Anaraki, Saeed Samet
However, traditional feature selection methods are only capable of processing centralized datasets and are not able to satisfy today's distributed data processing needs.
no code implementations • 17 Aug 2020 • Javad Rahimipour Anaraki, Jae Moon, Tom Chau
Brain-computer interface (BCI) aims to establish and improve human and computer interactions.
no code implementations • 4 Sep 2020 • Javad Rahimipour Anaraki, Silvia Orlandi, Tom Chau
The network classification accuracy was evaluated using leave-one-subject-out cross-validation.