Search Results for author:
Found 7 papers, 2 papers with code
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data
We propose a flexible and simple to use framework for generating time series data, which is aimed at developing, evaluating, and benchmarking time series causal discovery methods.
Unsuitability of NOTEARS for Causal Graph Discovery
Causal Discovery methods aim to identify a DAG structure that represents causal relationships from observational data.
Reliability and comparability of human brain structural covariance networks
In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults.
Neurons and Cognition Quantitative Methods
AREA: Adaptive Reference-set Based Evolutionary Algorithm for Multiobjective Optimisation
The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems.
A Scalable Test Suite for Continuous Dynamic Multiobjective Optimisation
It is demonstrated with empirical studies that the proposed test suite is more challenging to the dynamic multiobjective optimisation algorithms found in the literature.
The BioDynaMo Project
Computer simulations have become a very powerful tool for scientific research.
Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): Comparing multi-electrode recordings from simulated and biological mammalian cortical tissue
A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue.
