This highlights the importance of partitioning criteria for training a calibrated and accurate model. To validate the aforementioned analysis, we propose a method that involves jointly learning a semantic aware grouping function based on deep model features and logits to partition the data space into subsets. Subsequently, a separate calibration function is learned for each subset.

Since their inception, V ariational Autoencoders (V AEs) have become central in machine learning. Despite their widespread use, numerous questions regarding their theoretical properties remain open.

Current deep networks are very data-hungry and benefit from training on large-scale datasets, which are often time-consuming to collect and annotate.

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns.

This leads to a new MU paradigm, termed prune first, then unlearn, which infuses a sparse model prior into the unlearning process.

A central notion in practical and theoretical machine learning is that of a weak learner, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting.

Standard few-shot learning methods employ geometric similarity metrics such as cosine similarity and negative Euclidean distance to gauge the semantic relatedness between two features.