Recent Highly Cited Papers on Unsupervised Machine Learning

Generated with sparks and insights from 7 sources

Introduction

Recent Focus: The user is interested in recent academic papers on Unsupervised Learning in Machine Learning, specifically those published in the last year and cited at least 50 times.
Key Sources: Relevant sources include the Journal of Machine Learning Research, ScienceDirect, and ArXiv.
Example Papers: Some notable papers include 'Unsupervised machine learning in Urban Studies' and 'Unsupervised learning of mid-level visual representations'.
Citation Metrics: Papers with high citation counts are often found in reputable journals and repositories like JMLR and ArXiv.
Research Trends: Unsupervised learning is being applied in various fields such as urban studies and electronic health records (EHRs).

Unsupervised machine learning in urban studies: A systematic review revealing the broad applications of unsupervised learning in urban studies.
Unsupervised learning of mid-level visual representations: Discusses the rise of Self-Supervised Learning and its impact on Visual Data analysis.
Unsupervised Machine Learning for the Discovery: Highlights the use of unsupervised learning in identifying novel patterns from electronic health records (EHRs).

Journal of Machine Learning Research (JMLR): Provides an international forum for high-quality scholarly articles in all areas of machine learning.
ArXiv: A popular public repository of research papers, including those on machine learning.
ScienceDirect: Hosts numerous papers on unsupervised learning and other machine learning topics.

Self-Supervised Learning: Increasingly popular in recent years, focusing on learning representations from data without explicit labels.
Urban Studies: Unsupervised learning is being used to analyze urban data, revealing patterns and trends in city planning and development.
Electronic Health Records (EHRs): Unsupervised learning helps in discovering novel patterns and relations in medical data.

Urban Studies: Analyzing urban data to improve city planning and development.
Medical Data: Discovering patterns in electronic health records to enhance medical research and patient care.
Visual Data: Learning mid-level visual representations for better image and video analysis.

High Citation Counts: Papers with at least 50 citations are often found in reputable journals and repositories.
JMLR and ArXiv: Common sources for highly cited machine learning papers.
Recent Papers: Focus on papers published in the last year to ensure relevance and up-to-date information.