Giulia Denevi

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #16,936 of 35,345 by trust here, #17,412 by citations
• Computational Mechanics — 29 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Optimization & Training — 16 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Meta & Continual Learning — 12 citations into it
• Statistical Learning Theory — 10 citations into it
• Computer Networks and Communications — 8 citations into it; #4,368 of 9,886 by trust here, #3,638 by citations

Whom Giulia Denevi trusts overall

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

1. Andreas Maurer
2. Jonathan Baxter
3. Massimiliano Pontil
4. Anastasia Pentina
5. Elad Hazan
6. Shai Shalev-Shwartz
7. Andreas Argyriou
8. Arindam Banerjee
9. Nicolò Cesa-Bianchi
10. Theodoros Evgeniou

Who trusts Giulia Denevi overall

1. Mikhail Khodak
2. Kaiyi Ji
3. Maria-Florina Balcan
4. Nilesh Tripuraneni
5. Pan Zhou
6. Nikunj Saunshi
7. Junjie Yang
8. Ameet Talwalkar
9. Carlo Ciliberto
10. Liam Collins

Papers from Giulia Denevi's perspective

Ordered by how strongly the researchers Giulia Denevi trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning-to-Learn Stochastic Gradient Descent with Biased Regularization (2019) — 120 citations
• Incremental Learning-to-Learn with Statistical Guarantees (2018) — 48 citations
• Learning To Learn Around A Common Mean (2018) — 85 citations