Information Theoretic Generative AI
If you are working on generative AI with an information theoretic approach, you may be interested in taking a look at our recent work on: Mutual information estimation – NeurIPS 2024 [...]
Tutorial Statistical Learning with Generative Models for Communications
I am happy to deliver a tutorial on Statistical Learning with Generative Models for Communications at IEEE ICC 2023 on June 1st, 2023. Machine learning is gaining more and more interest in [...]
MIND: Optimal Mutual Information Based Neural Decoding
Optimal data detection and decoding in known channels is a well studied topic whose solution is given by the maximum a posteriori probability (MAP) criterion, which is optimal in a minimum error [...]
Statistical Learning with Generative Models for Communications
Learning the statistics of physical phenomena has been a long-time research objective. The advent of machine learning methods has offered effective tools to tackle such an objective in several [...]
Segmented Generative Models beyond GANs
Tremendous progress has been made in the development of generative networks that are capable of mimicking real data. The most exemplary approach is represented by GANs that have shown to work [...]