Marius Miron

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 118 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #2,861 of 35,345 by trust here, #2,908 by citations
• Safety, Robustness & Interpretability — 24 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it; #3,005 of 21,575 by trust here, #2,191 by citations
• Safety Research — 22 citations into it
• AI Ethics, Fairness & Society — 16 citations into it
• Sociology and Political Science — 16 citations into it
• Representation & Self-Supervised Learning — 10 citations into it; #397 of 3,145 by trust here, #385 by citations
• Signal Processing — 9 citations into it; #4,805 of 7,572 by trust here, #4,750 by citations

Whom Marius Miron trusts overall

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

1. Alex Krizhevsky
2. Alexandra Chouldechova
3. Zachary Chase Lipton
4. Geoffrey E. Hinton
5. Moritz Hardt
6. Yoshua Bengio
7. Yann LeCun
8. Sam Corbett-Davies
9. Matthew D. Zeiler
10. Faisal Kamiran

Who trusts Marius Miron overall

1. Zixuan Ke
2. Gyuhak Kim
3. Rahaf Aljundi
4. Bing Liu
5. Jaehong Yoon
6. Gobinda Saha
7. Tinne Tuytelaars
8. Matthias De Lange
9. Emilio Carrizosa
10. Gido M. van de Ven

Papers from Marius Miron's perspective

Ordered by how strongly the researchers Marius Miron 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Overcoming catastrophic forgetting with hard attention to the task (2018) — 1,304 citations
• Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia (2019) — 100 citations
• Addressing multiple metrics of group fairness in data-driven decision making (2020) — 9 citations
• Monoaural Audio Source Separation Using Deep Convolutional Neural Networks (2017) — 192 citations