Yani Ioannou

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 352 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 101 citations into it; #12,829 of 21,575 by trust here, #6,542 by citations
• Artificial Intelligence — 89 citations into it; #22,906 of 35,345 by trust here, #12,974 by citations
• Efficiency & Compression — 37 citations into it; #714 of 1,264 by trust here, #608 by citations
• Model Architectures — 37 citations into it
• Optimization & Training — 28 citations into it
• Scaling & Learning Theory — 8 citations into it
• Computer Networks and Communications — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Yani Ioannou trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Yann LeCun
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Karen Simonyan
7. Ilya Sutskever
8. Yoshua Bengio
9. X. Zhang
10. Shaoqing Ren

Who trusts Yani Ioannou overall

1. Georgios Georgiadis
2. Wujie Wen
3. Ruizhe Zhao
4. Xiangyu Zhang
5. Christoph Feichtenhofer
6. Simon Vandenhende
7. Saining Xie
8. Guotian Xie
9. Tejalal Choudhary
10. Ting Zhang

Papers from Yani Ioannou's perspective

Ordered by how strongly the researchers Yani Ioannou 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups (2016) — 295 citations
• Training CNNs with Low-Rank Filters for Efficient Image Classification (2015) — 156 citations
• Decision Forests, Convolutional Networks and the Models in-Between (2016) — 93 citations
• Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win (2020) — 108 citations
• Measuring Neural Net Robustness with Constraints (2016) — 451 citations