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Found 7 papers, 2 papers with code
Sequential inductive prediction intervals
In this paper we explore the concept of sequential inductive prediction intervals using theory from sequential testing.
Exploring Singularities in point clouds with the graph Laplacian: An explicit approach
We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of point clouds.
Concentration inequalities for leave-one-out cross validation
In this article we prove that estimator stability is enough to show that leave-one-out cross validation is a sound procedure, by providing concentration bounds in a general framework.
Deep limits and cut-off phenomena for neural networks
We consider dynamical and geometrical aspects of deep learning.
Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI
The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120, 000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years.
Approximation of BV functions by neural networks: A regularity theory approach
We first study the convergence to equilibrium of the stochastic gradient flow associated with the cost function with a quadratic penalization.
Neural ODEs as the Deep Limit of ResNets with constant weights
In this paper we prove that, in the deep limit, the stochastic gradient descent on a ResNet type deep neural network, where each layer shares the same weight matrix, converges to the stochastic gradient descent for a Neural ODE and that the corresponding value/loss functions converge.
