Machines are rapidly gaining the ability to perceive, interpret and interact with the visual world in ways that were once ...

Recently, Hongwei Li’s team from China University of Geosciences published a research article titled “scSCC: A swapped contrastive learning-based clustering method for single-cell gene ...

Scientists at Massachusetts Institute of Technology have devised a way for large language models to keep learning on the fly—a step toward building AI that continually improves itself.

Specifically, the proposed model contains two branches: contrastive learning and classification. The contrastive learning branch introduces the contrastive learning strategy to enhance the ...

Conformal prediction offers an alternative framework for representing machine learning outputs instead of point prediction scores. This approach has the potential to improve transparency and reduce ...

For multivariate time series classification, current research predominantly focuses on contrastive learning to acquire suitable representations. Despite their successes in enhancing accuracy and ...

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help ...

Researchers from MIT, Microsoft, and Google have introduced a “periodic table of machine learning” that stands to unify many different machine learning techniques using a single framework. Their ...

She noticed similarities between clustering and contrastive learning, another machine-learning method. As she explored the math behind both, she realized they could be explained using the same ...

This paper presents a comprehensive machine learning approach for credit score classification, addressing key challenges in financial risk assessment. We propose an optimized CatBoost-based framework ...