Deterministic insights from stochastic interactions

Speed-Density Relationship: From Deterministic to Stochastic

Traffic flow is a many-car system with complex and stochastic movement. It is difficult to describe the system dynamics solely using deterministic models which describe average system behaviors. Therefore, a stochastic speed-density relationship is proposed as a further step forward to overcome the well-known drawbacks of deterministic models. Modeling results show that by taking care of second...

Making Stochastic Neural Networks from Deterministic Ones

It has been believed that stochastic feedforward neural networks (SFNN) have several advantages beyond deterministic deep neural networks (DNN): they have more expressive power allowing multi-modal mappings and regularize better due to their stochastic nature. However, training SFNN is notoriously harder. In this paper, we aim at developing efficient training methods for large-scale SFNN, in pa...

From Deterministic to Stochastic Methods for Shape From Shading

Shape from shading designates the problem of shape rebuilding of a surface, starting from one image only of this surface. We compare different iterative methods of resolution of shape from shading. An analysis of the various discrete formulations of the problem is made, which allows the proposition of a certain number of error functions. Three deterministic methods to minimize these errors are ...

Deterministic Versus Stochastic Explanations

Oxygen isotope data for a Pliocene interval from 3.6 to 2.8 million years ago show a mean increase (0.5 per mil) of benthic 6180 at about 3.2 million years ago, whereas planktic 6180 does not increase. This lack of covariance indicates that the event at 3.2 million years did not result in a permanent increase in the ice budget of either the Northern or the Southern Hemisphere. The history of co...

Stochastic versus Deterministic Approaches