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Found 8 papers, 2 papers with code
The twin peaks of learning neural networks
Recent works demonstrated the existence of a double-descent phenomenon for the generalization error of neural networks, where highly overparameterized models escape overfitting and achieve good test performance, at odds with the standard bias-variance trade-off described by statistical learning theory.
The Mean Dimension of Neural Networks -- What causes the interaction effects?
We use the generalized total indices to produce heatmaps for post-hoc explanations, and we employ the mean dimension on the PCA-transformed features for cross comparisons of the artificial neural networks structures.
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.
Reconstruction of Pairwise Interactions using Energy-Based Models
Pairwise models like the Ising model or the generalized Potts model have found many successful applications in fields like physics, biology, and economics.
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.
Natural representation of composite data with replicated autoencoders
Generative processes in biology and other fields often produce data that can be regarded as resulting from a composition of basic features.
Inverse Statistical Physics of Protein Sequences: A Key Issues Review
In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved.
Improving contact prediction along three dimensions
Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members.
