Riccardo Volpi

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 491 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #14,032 of 35,345 by trust here, #9,369 by citations
• Computer Vision and Pattern Recognition — 100 citations into it; #17,072 of 21,575 by trust here, #14,822 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Optimization & Training — 26 citations into it
• Safety, Robustness & Interpretability — 19 citations into it; #2,017 of 2,251 by trust here, #1,047 by citations
• Evaluation & Benchmarks — 14 citations into it
• Model Architectures — 9 citations into it
• Reinforcement Learning — 7 citations into it

Whom Riccardo Volpi trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. Yaroslav Ganin
9. Ross B. Girshick
10. Jimmy Ba

Who trusts Riccardo Volpi overall

1. Kaiyang Zhou
2. Ryuichiro Hataya
3. Jiangwei Xie
4. Yongxin Yang
5. Mingfu Liang
6. Jan Zdenek
7. Haohan Wang
8. Prithvijit Chattopadhyay
9. Fengchun Qiao
10. Timothy M. Hospedales

Papers from Riccardo Volpi's perspective

Ordered by how strongly the researchers Riccardo Volpi 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning (2020) — 91 citations
• Curriculum Dropout (2017) — 121 citations
• Generalizing to Unseen Domains via Adversarial Data Augmentation (2018) — 918 citations
• Model Vulnerability to Distributional Shifts over Image Transformation Sets (2019) — 27 citations