Gianluca Bontempi

Artificial Intelligence · 1998–2019

A researcher in the Science corpus: 17 papers; trusts 157 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 160 citations into it; #13,390 of 35,345 by trust here, #11,750 by citations
• Molecular Biology — 78 citations into it; #4,492 of 17,773 by trust here, #8,480 by citations
• Information Systems — 57 citations into it
• Evaluation & Benchmarks — 38 citations into it
• Statistics and Probability — 37 citations into it
• Statistical Learning Theory — 30 citations into it
• Accounting — 28 citations into it
• Management Science and Operations Research — 25 citations into it; #4,654 of 11,946 by trust here, #2,957 by citations

Whom Gianluca Bontempi trusts overall

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

1. Nitesh V. Chawla
2. Paul J. Werbos
3. Charles Peter Elkan
4. J. Ross Quinlan
5. Tom Fawcett
6. Jaka Demsar
7. Trevor J. Hastie
8. Pedro M. Domingos
9. David J. Hand
10. Bianca Zadrozny

Who trusts Gianluca Bontempi overall

1. Souhaib Ben Taieb
2. A. Navon
3. Andrea Dal Pozzolo
4. Yingyi Chen
5. Kasun Bandara
6. Yosi Keller
7. Amir F. Atiya
8. Yinjun Wu
9. Hyun-Jae Choi
10. Vân Anh Huynh-Thu

Papers from Gianluca Bontempi's perspective

Ordered by how strongly the researchers Gianluca Bontempi trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Attention is All you Need (Neural Information Processing Systems)
7. Et al
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Calibrating Probability with Undersampling for Unbalanced Classification (2015) — 681 citations
• Information-Theoretic Inference of Large Transcriptional Regulatory Networks (2007) — 462 citations
• A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition (2011) — 700 citations
• minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information (2008) — 510 citations
• TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data (2015) — 3,480 citations
• Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity (2008) — 294 citations
• Machine Learning Strategies for Time Series Forecasting (2012) — 545 citations
• Multiple-output modeling for multi-step-ahead time series forecasting (2010) — 240 citations
• Combining unsupervised and supervised learning in credit card fraud detection (2019) — 452 citations
• New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx (2019) — 445 citations
• Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018) — 554 citations
• Determinants of community structure in the global plankton interactome (2015) — 901 citations
• Adaptive Machine Learning for Credit Card Fraud Detection (2015) — 104 citations
• Learned lessons in credit card fraud detection from a practitioner perspective (2014) — 537 citations
• Biological Processes Associated with Breast Cancer Clinical Outcome Depend on the Molecular Subtypes (2008) — 828 citations
• Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. (2007) — 829 citations
• Lazy Learning Meets the Recursive Least Squares Algorithm (1998)