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Found 8 papers, 1 papers with code
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.
Typical and atypical solutions in non-convex neural networks with discrete and continuous weights
We analyze the geometry of the landscape of solutions in both models and find important similarities and differences.
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.
Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry
This lets us derive a meaningful notion of the flatness of minimizers and of the geodesic paths connecting them.
Deep learning via message passing algorithms based on belief propagation
Message-passing algorithms based on the Belief Propagation (BP) equations constitute a well-known distributed computational scheme.
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.
Entropic gradient descent algorithms and wide flat minima
The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time.
