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The micro-project investigated recent machine and deep learning-based methods for modeling event interactions through temporal processes.
We focus on studying prediction problems from event sequences. The latter are ubiquitous in several scenarios involving human activities, including especially information diffusion in social media.
The scope of the MP is to investigate methods for learning deep probabilistic models based on latent representations that can explain and predict event evolution within social media. Latent variables are particularly promising in situations where the level of uncertainty is high, due to their capabilities in modeling the hidden causal relationships that characterize data and ultimately guarantee robustness and trustability in decisions. In addition, probabilistic models can efficiently support simulation, data generation, and different forms of collaborative human-machine reasoning.
There are several reasons why this problem is challenging. We plan to study these challenges and provide an overview of the current advances, as well as a repository of available techniques and datasets that can be exploited for research and study.
This Humane-AI-Net micro-project was carried out by Consiglio Nazionale delle Ricerche (CNR) and Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) and the University of Pisa (UNIPI).
The project is part of the Humane-AI-Net network of excellent research centres in AI. It contributes to this network in the following aspects:
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