Can scientists detect life without knowing what it looks like? Research using machine learning offers a new way

LifeTracer is not a universal life detector. Rather, it provides a foundation for interpreting complex organic mixtures. The Bennu findings remind us that life-friendly chemistry may be widespread ...
IEEE

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection

Abstract: The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustness ...
The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...
GitHub

krpraful/Artificial-Intelligence-Project-List-with-Code

This list is continuously updated. Pull requests welcome — please follow CONTRIBUTING.md. A curated list of 500+ AI/ML/DL/CV/NLP projects and resources (tutorials, repos, datasets, papers-with-code).
GitHub

Real-time machine monitoring and anomaly detection using Machine Learning

An Industrial IoT (IIoT) predictive maintenance system that simulates factory equipment, monitors sensor data in real-time, and uses Machine Learning to predict equipment failures before they happen ...
Edutopia

Making Project-Based Learning More Impactful by Emphasizing Depth Over Speed

In education, there’s sometimes a misperception that innovation must be linked to major changes in classroom instruction. I’ve witnessed schools restructuring their classes to be entirely ...
IEEE

Positive Sample Mining: Fuzzy Threshold-Based Contrastive Learning for Enhanced Unsupervised Skeleton-Based Action Recognition

Abstract: Contrastive learning is one of the fundamental paradigms for unsupervised 3-D skeleton-based action recognition. Existing contrastive learning paradigms typically enhance model ...