Time series forecasting | Ricerc@Sapienza

A non-parametric softmax for improving neural attention in time-series forecasting

Neural attention has become a key component in many deep learning applications, ranging from machine translation to time series forecasting. While many variations of attention have been developed over recent years, all share a common component in the application of a softmax function to normalize the attention weights, in order to transform them into valid mixing coefficients.

On the possibility of predicting glycaemia ‘on the fly’ with constrained IoT devices in type 1 diabetes mellitus patients

Type 1 Diabetes Mellitus (DM1) patients are used to checking their blood glucose levels several times per day through finger sticks and, by subjectively handling this information, to try to predict their future glycaemia in order to choose a proper strategy to keep their glucose levels under control, in terms of insulin dosages and other factors.

Modeling and forecasting gender-based violence through machine learning techniques

Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels.