Andrew Rouditchenko

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 338 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 133 citations into it
• Multimodal Learning — 74 citations into it; #579 of 1,211 by trust here, #483 by citations
• Artificial Intelligence — 71 citations into it
• Representation & Self-Supervised Learning — 64 citations into it
• Signal Processing — 60 citations into it; #3,297 of 7,572 by trust here, #874 by citations
• Cognitive Neuroscience — 8 citations into it; #4,090 of 10,852 by trust here, #2,376 by citations
• Model Architectures — 8 citations into it
• Developmental Biology — 7 citations into it

Whom Andrew Rouditchenko trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Andrew Zisserman
5. Alex Krizhevsky
6. Antonio Torralba
7. Yoshua Bengio
8. Ross B. Girshick
9. Yann LeCun
10. Ilya Sutskever

Who trusts Andrew Rouditchenko overall

1. Ruohan Gao
2. Chuang Gan
3. Mandela Patrick
4. Hao Zhu
5. Kristen Grauman
6. Triantafyllos Afouras
7. Andrew Zisserman
8. Yuying Ge
9. Jes'us Andr'es Portillo-Quintero
10. Hang Zhao

Papers from Andrew Rouditchenko's perspective

Ordered by how strongly the researchers Andrew Rouditchenko 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• AVLnet: Learning Audio-Visual Language Representations from Instructional Videos (2020) — 148 citations
• Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos (2021) — 100 citations
• Self-supervised Audio-visual Co-segmentation (2019) — 106 citations
• The Sound of Pixels (2018) — 599 citations