To avoid failures on out-of-distribution data, recent works have sought to extract features that
have an invariant or stable relationship with the label across domains, discarding" spurious" …

There has been an explosion of data collected about sports. Because such data is extremely
rich and complex, machine learning is increasingly being used to extract actionable insights …

Objective We developed an artificial intelligence (AI)‐based headache diagnosis model
using a large questionnaire database from a headache‐specializing clinic. Background …

Calibrating deep neural models plays an important role in building reliable, robust AI
systems in safety-critical applications. Recent work has shown that modern neural networks …

Traditional fully-deterministic algorithms, which rely on physical equations and mathematical
models, are the backbone of many scientific disciplines for decades. These algorithms are …

Automated machine learning (AutoML) is a young research area aiming at making high-
performance machine learning techniques accessible to a broad set of users. This is …

Machine learning (ML) systems have enabled the modelling of quantitative structure–
property relationships (QSPR) and structure-activity relationships (QSAR) using existing …

Scientific inquiry has long relied on deterministic algorithms for systematic problem-solving
and predictability. However, the rise of artificial intelligence (AI) has revolutionized data …

Firearm violence results in thousands of victims annually in Brazil, which is ascribed, among
other factors, to the low homicide resolution rates. One method for generating forensic …

Abstract Heating, Ventilation, and Air Conditioning (HVAC) systems in large-scale buildings
often struggle to ensure satisfactory thermal comfort for diverse occupants while minimizing …