British Journal of Ophthalmology

Identifying patients with poor visual outcomes after primary rhegmatogenous retinal detachment surgery using machine learning

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...
Compliance WeekOpinion

AI in compliance: Are we still driving, or just riding shotgun?

In-depth analysis and peer practice for 10,000+ risk and compliance leaders. HQP benchmarking, LPEC certification, and the ...
IEEE

Face recognition based on fitting a 3D morphable model

Abstract: This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and ...
BMJ Open

Primary care use among adults with eating disorders in England: a population-based cohort study using electronic health records

Objectives To examine primary care contacts among individuals with eating disorders (EDs) and assess differences across ...
www.cs.cmu.edu

Liu Yang

Buy-in-Bulk Active Learning. Liu Yang and Jaime Carbonell. Advances in Neural Information Processing Systems 26 (NIPS), 2013. Liu Yang, Avrim Blum and Jaime Carbonell. Learnability of DNF with ...
AHA/ASA Journals

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...
IEEE

50 Years of Automated Face Recognition

Abstract: Over the past five decades, automated face recognition (FR) has progressed from handcrafted geometric and statistical approaches to advanced deep learning architectures that now approach, ...