The desire to reduce the dependence on curated, labeled datasets and to leverage the vast
quantities of unlabeled data has triggered renewed interest in unsupervised (or self …

In traditional supervised learning, one uses” labeled” data to build a model. However,
labeling the training data for real-world applications is difficult, expensive, or time …

Multiple modalities often co-occur when describing natural phenomena. Learning a joint
representation of these modalities should yield deeper and more useful representations …

Within months of birth, children develop meaningful expectations about the world around
them. How much of this early knowledge can be explained through generic learning …

A central goal of unsupervised learning is to acquire representations from unlabeled data or
experience that can be used for more effective learning of downstream tasks from modest …

Humans learn from visual inputs at multiple timescales, both rapidly and flexibly acquiring
visual knowledge over short periods, and robustly accumulating online learning progress …

Self-supervised learning (SSL) has emerged as a powerful framework to learn
representations from raw data without supervision. Yet in practice, engineers face issues …

A major goal of unsupervised learning is to discover data representations that are useful for
subsequent tasks, without access to supervised labels during training. Typically, this …

Self-supervised contrastive learning is a powerful tool to learn visual representation without
labels. Prior work has primarily focused on evaluating the recognition accuracy of various …

Machine learning (ML) is a data-driven strategy in which computers learn from data without
human intervention. The outstanding ML applications are used in a variety of areas. In ML …