Persona2vec: a flexible multi-role representations learning framework for graphs

Joint auto-encoders: a flexible multi-task learning framework

The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest. Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learn...

A nonparametric Bayesian framework for constructing flexible feature representations.

Representations are a key explanatory device used by cognitive psychologists to account for human behavior. Understanding the effects of context and experience on the representations people use is essential, because if two people encode the same stimulus using different representations, their response to that stimulus may be different. We present a computational framework that can be used to de...

An efficient framework for learning sentence representations

In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classification problem. Given a sentence and the context in which it appears, a class...

A Probabilisti Framework for Multi-Task Learning

Towards a Flexible Development Framework for Multi-Agent Systems

In this paper, we present a software framework, called HDS (Heterogeneous Distributed System), that tries to simplify the realization of distributed applications and, in particular, of multi-agent systems, by: i) abstracting the use of different technologies for the realization of distributed applications on networks of heterogeneous devices connected through a set of different communication tr...