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Found 7 papers, 2 papers with code
On the Stability of a non-hyperbolic nonlinear map with non-bounded set of non-isolated fixed points with applications to Machine Learning
This paper deals with the convergence analysis of the SUCPA (Semi Unsupervised Calibration through Prior Adaptation) algorithm, defined from a first-order non-linear difference equations, first developed to correct the scores output by a supervised machine learning classifier.
Invariant Representations in Deep Learning for Optoacoustic Imaging
Image reconstruction in optoacoustic tomography (OAT) is a trending learning task highly dependent on measured physical magnitudes present at sensing time.
Cross-domain Sentiment Classification in Spanish
In this work we perform a study on the ability of a classification system trained with a large database of product reviews to generalize to different Spanish domains.
Combining band-frequency separation and deep neural networks for optoacoustic imaging
In this paper we consider the problem of image reconstruction in optoacoustic tomography.
PACMAN: PAC-style bounds accounting for the Mismatch between Accuracy and Negative log-loss
In this work, we introduce an analysis based on point-wise PAC approach over the generalization gap considering the mismatch of testing based on the accuracy metric and training on the negative log-loss.
The Role of Mutual Information in Variational Classifiers
In practice, this behaviour is controlled by various--sometimes heuristics--regularization techniques, which are motivated by developing upper bounds to the generalization error.
Understanding the Behaviour of the Empirical Cross-Entropy Beyond the Training Distribution
Our main result is that the testing gap between the empirical cross-entropy and its statistical expectation (measured with respect to the testing probability law) can be bounded with high probability by the mutual information between the input testing samples and the corresponding representations, generated by the encoder obtained at training time.
