activity
20162020
most citedA hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy

3 citations · 5 across the 7 of their papers we have counts for

collaborators

9 papers

stat.ME2020

Efficient Quantile Tracking Using an Oracle

Hugo L. Hammer, Anis Yazidi, Michael A. Riegler +1

For incremental quantile estimators the step size and possibly other tuning parameters must be carefully set. However, little attention has been given on how to set these values in…

eess.SP20203 cited

A hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy

Marco A. Pinto-Orellana, Diego C. Nascimento, Peyman Mirtaheri +3

In the current paper, we introduce a parametric data-driven model for functional near-infrared spectroscopy that decomposes a signal into a series of independent, rescaled, time-sh…

stat.ME2020

Estimating Tukey Depth Using Incremental Quantile Estimators

Hugo Lewi Hammer, Anis Yazidi, Håvard Rue

The concept of depth represents methods to measure how deep an arbitrary point is positioned in a dataset and can be seen as the opposite of outlyingness. It has proved very useful…

cs.NE20191 cited

A general representation of dynamical systems for reservoir computing

Sidney Pontes-Filho, Anis Yazidi, Jianhua Zhang +5

Dynamical systems are capable of performing computation in a reservoir computing paradigm. This paper presents a general representation of these systems as an artificial neural net…

stat.ME2019

Joint Tracking of Multiple Quantiles Through Conditional Quantiles

Hugo Lewi Hammer, Anis Yazidi, Håvard Rue

Estimation of quantiles is one of the most fundamental real-time analysis tasks. Most real-time data streams vary dynamically with time and incremental quantile estimators document…

stat.ME2019

Quantile Tracking in Dynamically Varying Data Streams Using a Generalized Exponentially Weighted Average of Observations

Hugo Lewi Hammer, Anis Yazidi, Håvard Rue

The Exponentially Weighted Average (EWA) of observations is known to be state-of-art estimator for tracking expectations of dynamically varying data stream distributions. However,…