In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL methods rely on heuristics or predefined rules for generating pseudo-labels and leveraging unlabeled data.

The inferred user preference descriptions are used to define prompts for generating responses in the future.

Building on this, our study investigates the interplay between sharpness and diversity within deep ensembles, illustrating their crucial role in robust generalization to both in-distribution (ID) and out-of-distribution (OOD) data.

Artificial intelligence systems are trained combining various observational and experimental datasets from different source sites, and are increasingly used to reason about the effectiveness of candidate policies.

Pre-trained models ( e.g ., foundation models) have become an indispensable component in modern

Considering the fact that different types of degraded polarized snapshots would provide complementary knowledge, i.e ., the short-exposure noisy ones tend to be clear while the long-exposure blurry Most of this work was done as a PhD student at Peking University.

Symmetries have proven useful in machine learning models, improving generalisation and overall performance.

Machine learning (ML) has achieved remarkable success in prediction tasks.

However, the complexity and duration of procedural activities inherently increase the likelihood of making errors.