Roberto Confalonieri
Artificial Intelligence · 2005–2023
A researcher in the Science corpus: 13 papers; trusts 27 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 219 citations into it
- Safety, Robustness & Interpretability — 101 citations into it
- Information Systems — 37 citations into it
- Plant Science — 34 citations into it; #7,647 of 8,232 by trust here, #3,695 by citations
- Statistics and Probability — 34 citations into it
- Computational Theory and Mathematics — 20 citations into it
- Management Science and Operations Research — 19 citations into it
- Ecology, Evolution, Behavior and Systematics — 18 citations into it; #6,189 of 6,648 by trust here, #3,347 by citations
Whom Roberto Confalonieri trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Marco Tulio Ribeiro
- Hirotugu Akaike
- Chris Russell
- Geoffrey E. Hinton
- William R. Swartout
- Sameer Singh
- Daniel D. Lee
- Scott M. Lundberg
- Mark W. Craven
- Geoffrey G. Towell
Who trusts Roberto Confalonieri overall
Papers from Roberto Confalonieri's perspective
Ordered by how strongly the researchers Roberto Confalonieri trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
Their papers
- Comparison of sensitivity analysis techniques: A case study with the rice model WARM (2010) — 223 citations
- Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence (2023) — 1,437 citations
- Sensitivity analysis using Morris: Just screening or an effective ranking method? (2021) — 51 citations
- Comparison of three calibration methods for modeling rice phenology (2020) — 63 citations
- A historical perspective of explainable Artificial Intelligence (2020) — 324 citations
- Sensitivity of WOFOST-based modelling solutions to crop parameters under climate change (2018) — 51 citations
- Perfunctory analysis of variance in agronomy, and its consequences in experimental results interpretation (2012) — 52 citations
- Sensitivity analysis for a complex crop model applied to Durum wheat in the Mediterranean. (2010) — 101 citations
- A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions (2010) — 60 citations
- Monte Carlo based sensitivity analysis of two crop simulators and considerations on model balance (2010) — 55 citations
- Multi-Metric Evaluation of the Models WARM, CropSyst, and WOFOST for Rice (2009) — 136 citations
- Evaluation of LAI-2000 for leaf area index monitoring in paddy rice (2006) — 85 citations
- Evaluation of CropSyst for simulating the yield of flooded rice in northern Italy (2005) — 99 citations