Michele Donini

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 26 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #12,045 of 35,345 by trust here, #10,430 by citations
• Optimization & Training — 29 citations into it; #1,321 of 2,601 by trust here, #1,059 by citations
• Safety, Robustness & Interpretability — 29 citations into it
• AI Ethics, Fairness & Society — 25 citations into it; #264 of 494 by trust here, #196 by citations
• Safety Research — 23 citations into it; #2,515 of 5,829 by trust here, #1,792 by citations
• Computational Mechanics — 13 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Michele Donini trusts overall

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

1. Faisal Kamiran
2. Toon Calders
3. Theodoros Evgeniou
4. Yoshua Bengio
5. Yuhai Wu
6. Moritz Hardt
7. Diederik P. Kingma
8. Muhammad Bilal Zafar
9. Geoffrey E. Hinton
10. Massimiliano Pontil

Who trusts Michele Donini overall

1. Luca Oneto
2. Andrew Cotter
3. Robert C. Williamson
4. Silvia Chiappa
5. Serena Wang
6. Heinrich Jiang
7. Sina Baharlouei
8. Luca Franceschi
9. Muhammad Bilal Zafar
10. Ray Jiang

Papers from Michele Donini's perspective

Ordered by how strongly the researchers Michele Donini 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Forward and Reverse Gradient-Based Hyperparameter Optimization (2017) — 477 citations
• Empirical Risk Minimization under Fairness Constraints (2018) — 500 citations
• Taking Advantage of Multitask Learning for Fair Classification (2018) — 70 citations
• Distributed variance regularized Multitask Learning (2016) — 6 citations