– Europe/Lisbon — Online
Sala P3.10, Pavilhão de Matemática
Matemática para Inteligência Artificial
Elena Agliari, Sapienza Università di Roma.
The Hopfield model across Disordered Systems, Memory, and Machine Learning.
The Hopfield model stands as a paradigm at the intersection of statistical physics, theoretical neuroscience, and machine learning. Originally introduced as a biologically inspired model of associative memory, it has since evolved into a foundational framework for understanding a wide range of complex systems.
On the one hand, its roots in neuroscience enable a fruitful cross-fertilization: biologically grounded mechanisms continue to inspire algorithmic refinements and performance improvements in modern associative memory models. On the other hand, its formal connection with Boltzmann machines provides a bridge to contemporary machine learning techniques, including strategies such as dropout, pre-training, and the optimization of activation functions.
From the perspective of statistical mechanics, the Hopfield model remains a cornerstone for the analytical study of high-dimensional systems with disorder and frustration. This viewpoint naturally extends to the investigation of structured datasets, where the model offers a tractable yet expressive starting point for developing analytical insights.
In this talk, after a gentle introduction to the model, we will highlight some of these current research directions, while keeping the presentation accessible to a non-technical audience.