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Found 6 papers, 4 papers with code
$Γ$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
Natural systems with emergent behaviors often organize along low-dimensional subsets of high-dimensional spaces.
The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold
We develop information-geometric techniques to analyze the trajectories of the predictions of deep networks during training.
A picture of the space of typical learnable tasks
We develop information geometric techniques to understand the representations learned by deep networks when they are trained on different tasks using supervised, meta-, semi-supervised and contrastive learning.
Jeffrey's prior sampling of deep sigmoidal networks
Neural networks have been shown to have a remarkable ability to uncover low dimensional structure in data: the space of possible reconstructed images form a reduced model manifold in image space.
Canonical Sectors and Evolution of Firms in the US Stock Markets
A classification of companies into sectors of the economy is important for macroeconomic analysis and for investments into the sector-specific financial indices and exchange traded funds (ETFs).
Improvements to the Levenberg-Marquardt algorithm for nonlinear least-squares minimization
We introduce several improvements to the Levenberg-Marquardt algorithm in order to improve both its convergence speed and robustness to initial parameter guesses.
Data Analysis, Statistics and Probability Computational Physics
