Linear

Exploring Non-Linear Interactions in Multivariate Time Series

Hernando Ombao, Professor, Statistics

Nov 5, 12:00 - 13:00

KAUST

Linear Multivariate extremes Bayesian computational statistics

Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.

Why Today’s Neural Networks Still Use the 1943 McCullosh and Pitt’s Neuron Model?

Prof. Moncef Gabbouj, Department of Computing Sciences, Tampere University

May 15, 14:00 - 15:00

B1 L3 R3119

Linear neural network

Operational Neural Networks (ONNs) are new generation network models targeting to address two major drawbacks of conventional Convolutional Neural Networks (CNNs): the homogenous network configuration and the “linear” neuron model that can only perform linear transformations over previous layer outputs. ONNs can perform any linear or non-linear transformation with a proper combination of “nodal” and “pool” operators.