As an attempt to extract understanding from a population of alternative solutions, many methods exist to establish a consensus among them in the form of a single partition "point estimate" that summarizes the whole distribution.
Out-of-distribution (OOD) testing is increasingly popular for evaluating a machine learning system's ability to generalize beyond the biases of a training set.
This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing.
A plethora of research has been done in the past focusing on predicting student's performance in order to support their development.
The use of Robust Lasso-Zero is showcased for variable selection with missing values in the covariates.
We argue that the constrained optimization method of Rezende and Viola, 2018 is a lot more appropriate for training lossy compression models because it allows us to obtain the best possible rate subject to a distortion constraint.
Neuromorphic computing is henceforth a major research field for both academic and industrial actors.
In real-world applications, learning from data with multi-view and multi-label inevitably confronts with three challenges: missing labels, incomplete views, and non-aligned views.