Second-order networks in PyTorch

International audienceClassification of Symmetric Positive Definite (SPD) matrices is gaining momentum in a variety machine learning application fields. In this work we propose a Python library which implements neural networks on SPD matrices, based on the popular deep learning framework Pytorch

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Brain–computer interfaces (BCI) (also referred to as brain–machine interfaces; BMI) are, by definition, an interface between the human brain and a technological application. Brain activity for interpretation by the BCI can be acquired with either invasive or non-invasive methods. The key point is that the signals that are interpreted come directly from the brain, bypassing sensorimotor output channels that may or may not have impaired function. This paper provides a concise glimpse of the breadth of BCI research and development topics covered by the workshops of the 6th International Brain–Computer Interface Meeting

Is affine invariance well defined on SPD matrices? A principled continuum of metrics

International audienceSymmetric Positive Definite (SPD) matrices have been widelyused in medical data analysis and a number of different Riemannian met-rics were proposed to compute with them. However, there are very fewmethodological principles guiding the choice of one particular metric fora given application. Invariance under the action of the affinetransfor-mations was suggested as a principle. Another concept is based on sym-metries. However, the affine-invariant metric and the recently proposedpolar-affine metric are both invariant and symmetric. Comparing thesetwo cousin metrics leads us to introduce much wider families: power-affineand deformed-affine metrics. Within this continuum, we investigate otherprinciples to restrict the family size