Machine learning is an integral part of high-stakes decision-making in a broad swath of human-computer interactions. You ...

New spin on speculative decoding works with any model - now built into Transformers We all know that AI is expensive, but a ...

Just as people from different countries speak different languages, AI models also create various internal "languages"—a ...

The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical ...

Psychological testing has evolved from Freud to AI and eye tracking. Technology adds depth, but theory and ethics must guide its use to ensure meaningful and equitable diagnoses.

Manufacturers are willing to incorporate Machine Learning (ML) algorithms into their systems, especially those considered as Safety-Critical Systems (SCS). ML algorithms that perform binary ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

SVM is a machine learning algorithm used to address binary classification problems (Zhang et al., 2013). It maps the original vectors into a higher-dimensional space and constructs a hyperplane with ...

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 ...

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 scientists ...

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 ...