Ricardo José Gabrielli Barreto Campello

Artificial Intelligence · 2006–2019

A researcher in the Science corpus: 8 papers; trusts 55 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 101 citations into it; #6,548 of 35,345 by trust here, #9,641 by citations
• Signal Processing — 37 citations into it; #1,727 of 7,572 by trust here, #2,400 by citations
• Statistics and Probability — 34 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it; #14,369 of 21,575 by trust here, #15,162 by citations
• Information Systems — 17 citations into it
• Statistical and Nonlinear Physics — 12 citations into it
• Biomedical Engineering — 10 citations into it; #7,176 of 10,734 by trust here, #9,262 by citations
• Evaluation & Benchmarks — 10 citations into it

Whom Ricardo José Gabrielli Barreto Campello trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Hans-Peter Kriegel
2. Arthur Zimek
3. Anil K. Jain
4. Peer Kröger
5. Kimmo Vehkalahti
6. Jörg Sander
7. Sanghamitra Bandyopadhyay
8. Davoud Moulavi
9. Martin Ester
10. Erich Schubert

Who trusts Ricardo José Gabrielli Barreto Campello overall

1. Leland McInnes
2. D. Angelov
3. John Healy
4. Davoud Moulavi
5. Jörg Sander
6. Hongzhi Wang
7. Michael Hahsler
8. Manuel J. Sánchez-Franco
9. Arthur Zimek
10. Zhaoyi Xu

Papers from Ricardo José Gabrielli Barreto Campello's perspective

Ordered by how strongly the researchers Ricardo José Gabrielli Barreto Campello trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Algorithms for Clustering Data
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. LOF: identifying density-based local outliers
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014) — 302 citations
• Density‐based clustering (2019) — 578 citations
• Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015) — 853 citations
• Density-Based Clustering Validation (2014) — 330 citations
• Density-Based Clustering Based on Hierarchical Density Estimates (2013) — 2,572 citations
• Relative clustering validity criteria: A comparative overview (2010) — 365 citations
• A Survey of Evolutionary Algorithms for Clustering (2009) — 755 citations
• A fuzzy extension of the silhouette width criterion for cluster analysis (2006) — 300 citations