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Sparse Representations, Inference and Learning
In recent years statistical physics has proven to be a valuable tool to probe into large dimensional inference problems such as the ones occurring in machine learning.
The star-shaped space of solutions of the spherical negative perceptron
Empirical studies on the landscape of neural networks have shown that low-energy configurations are often found in complex connected structures, where zero-energy paths between pairs of distant solutions can be constructed.
Storage and Learning phase transitions in the Random-Features Hopfield Model
The Hopfield model is a paradigmatic model of neural networks that has been analyzed for many decades in the statistical physics, neuroscience, and machine learning communities.
Learning through atypical "phase transitions" in overparameterized neural networks
Current deep neural networks are highly overparameterized (up to billions of connection weights) and nonlinear.
Unveiling the structure of wide flat minima in neural networks
The success of deep learning has revealed the application potential of neural networks across the sciences and opened up fundamental theoretical problems.
