Nature

Self-Supervised Learning Applications in Medical Image Analysis

Self-supervised learning has emerged as a powerful paradigm to bridge the gap between data abundance and label scarcity in medical imaging. By constructing supervisory signals from the data ...
BIOENGINEER

Hybrid Deep Learning Model Boosts Prediction of Slope Stability Safety Factor

A hybrid deep learning framework that combines unsupervised feature extraction with supervised regression outperformed a ...
Nature

Self-Supervised Learning Applications in Medical Image Analysis articles from across Nature Portfolio

Deep learning (DL) models have reached remarkable achievements in medical imaging, but their performance heavily depends on the availability of large and diverse ...
Unite.AI

What Is Self-Supervised Learning? How Models Learn from Unlabeled Data

Self-supervised learning creates training targets from the structure of unlabeled data, allowing models to learn ...
BIOENGINEER

A generalized supervised contrastive learning framework for integrative multi-omics prediction models

A new machine-learning framework called MB-SupConCon extends supervised contrastive learning to continuous responses, ...
Yahoo Finance

The Global Self-Supervised Learning Market size is expected to reach $51.7 billion by 2028, rising at a market growth of 33.3% CAGR during the forecast period

New York, Oct. 20, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Self-supervised Learning Market Size, Share & Industry Trends Analysis Report By End-use, By ...
Purdue University

Machine Learning Fundamentals: Principles and Applications

Gain a deeper understanding of artificial intelligence with Machine Learning Fundamentals: Principles and Applications. This course explores core concepts and practical uses of supervised and ...