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Found 4 papers, 1 papers with code
Confidence Intervals for Selected Parameters
Standard, unadjusted confidence intervals for location parameters have the correct coverage probability for $k=1$, $m=2$ if, when the true parameters are zero, the estimators are exchangeable and symmetric.
Cautious Deep Learning
Our construction is based on $p(x|y)$ rather than $p(y|x)$ which results in a classifier that is very cautious: it outputs the null set --- meaning "I don't know" --- when the object does not resemble the training examples.
A Generalization of Convolutional Neural Networks to Graph-Structured Data
This paper introduces a generalization of Convolutional Neural Networks (CNNs) from low-dimensional grid data, such as images, to graph-structured data.
Interpretation of Prediction Models Using the Input Gradient
State of the art machine learning algorithms are highly optimized to provide the optimal prediction possible, naturally resulting in complex models.
