M. Perrone‐Bertolotti

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 9 citations into it

Whom M. Perrone‐Bertolotti trusts overall

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

1. Jerome H. Friedman
2. Eric R. Ziegel
3. Tianqi Chen
4. Gavin C. Cawley
5. Qingsong Xu

Papers from M. Perrone‐Bertolotti's perspective

Ordered by how strongly the researchers M. Perrone‐Bertolotti trusts endorse each paper. Same corpus, a different order for every researcher.

1. Greedy function approximation: A gradient boosting machine.
2. The Elements of Statistical Learning
3. Stochastic gradient boosting
4. Scikit-learn: Machine Learning in Python
5. XGBoost: A Scalable Tree Boosting System
6. Practical Lessons from Predicting Clicks on Ads at Facebook
7. An empirical comparison of supervised learning algorithms
8. Monte Carlo cross validation
9. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. (Cerebral Cortex)
10. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Their papers

• Probabilistic mapping of language networks from high frequency activity induced by direct electrical stimulation (2020)
• From intentions to actions: Neural oscillations encode motor processes through phase, amplitude and phase‐amplitude coupling (2017)
• Machine learning–XGBoost analysis of language networks to classify patients with epilepsy (2017) — 425 citations