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Found 7 papers, 2 papers with code
TorchCP: A Library for Conformal Prediction based on PyTorch
TorchCP is a Python toolbox for conformal prediction research on deep learning models.
Does Confidence Calibration Help Conformal Prediction?
Conformal prediction, as an emerging uncertainty qualification technique, constructs prediction sets that are guaranteed to contain the true label with high probability.
Resolution invariant deep operator network for PDEs with complex geometries
Neural operators (NO) are discretization invariant deep learning methods with functional output and can approximate any continuous operator.
Conformal Prediction for Deep Classifier via Label Ranking
In this paper, we empirically and theoretically show that disregarding the probabilities' value will mitigate the undesirable effect of miscalibrated probability values.
Koopman operator learning using invertible neural networks
In Koopman operator theory, a finite-dimensional nonlinear system is transformed into an infinite but linear system using a set of observable functions.
Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning
This paper proposes Friedrichs learning as a novel deep learning methodology that can learn the weak solutions of PDEs via a minmax formulation, which transforms the PDE problem into a minimax optimization problem to identify weak solutions.
Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm
Unfortunately, an undesirable characteristic of these methods is that the order of the filters grows linearly with the number of input data.
