Fosca Giannotti
Artificial Intelligence · 2007–2021
A researcher in the Science corpus: 15 papers; trusts 259 researchers and is trusted by 44 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 305 citations into it; #5,957 of 35,345 by trust here, #3,192 by citations
- Safety, Robustness & Interpretability — 169 citations into it; #556 of 2,251 by trust here, #269 by citations
- Statistical and Nonlinear Physics — 113 citations into it; #4,032 of 10,127 by trust here, #3,382 by citations
- Information Systems — 82 citations into it; #4,424 of 8,771 by trust here, #3,315 by citations
- Computer Vision and Pattern Recognition — 80 citations into it
- Molecular Biology — 48 citations into it; #6,868 of 17,773 by trust here, #15,531 by citations
- Transportation — 39 citations into it; #1,828 of 2,062 by trust here, #227 by citations
- AI Ethics, Fairness & Society — 38 citations into it
Whom Fosca Giannotti trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mark Newman
- Marco Tulio Ribeiro
- Santo Fortunato
- Geoffrey E. Hinton
- Dino Pedreschi
- Yoshua Bengio
- Alex Krizhevsky
- Riccardo Guidotti
- Karen Simonyan
- Diederik P. Kingma
Who trusts Fosca Giannotti overall
- Yu Zheng
- Carl Mooney
- Anna Monreale
- Hocine Cherifi
- Dino Pedreschi
- Jie Feng
- Takeshi Kurashima
- Riccardo Guidotti
- Xin Lu
- Yang Ye
Papers from Fosca Giannotti's perspective
Ordered by how strongly the researchers Fosca Giannotti trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- A Survey of Methods for Explaining Black Box Models (2018) — 5,098 citations
- Local Rule-Based Explanations of Black Box Decision Systems (2018) — 498 citations
- Benchmarking and survey of explanation methods for black box models (2021) — 345 citations
- Discrimination- and privacy-aware patterns (2015) — 97 citations
- A classification for community discovery methods in complex networks (2011) — 370 citations
- Meaningful Explanations of Black Box AI Decision Systems (2019) — 225 citations
- Factual and Counterfactual Explanations for Black Box Decision Making (2019) — 308 citations
- Open the Black Box Data-Driven Explanation of Black Box Decision Systems (2018) — 39 citations
- Returners and explorers dichotomy in human mobility (2015) — 517 citations
- Smart cities of the future (2012) — 1,059 citations
- DEMON: a local-first discovery method for overlapping communities (2012) — 266 citations
- Human mobility, social ties, and link prediction (2011) — 723 citations
- Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011) — 335 citations
- WhereNext: a location predictor on trajectory pattern mining (2009) — 640 citations
- Trajectory pattern mining (2007) — 1,171 citations