Simon Vandenhende

Computer Vision and Pattern Recognition · 2019–2024

A researcher in the Science corpus: 6 papers; trusts 1,030 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 214 citations into it; #9,415 of 21,575 by trust here, #7,499 by citations
• Artificial Intelligence — 175 citations into it; #14,602 of 35,345 by trust here, #13,353 by citations
• Representation & Self-Supervised Learning — 74 citations into it
• Model Architectures — 33 citations into it
• Optimization & Training — 25 citations into it
• Media Technology — 15 citations into it
• Efficiency & Compression — 14 citations into it
• Natural Language Processing — 14 citations into it

Whom Simon Vandenhende trusts overall

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

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

Who trusts Simon Vandenhende overall

1. Michael Crawshaw
2. Rahul Ramesh
3. Ximeng Sun
4. Menelaos Kanakis
5. Pratik Chaudhari
6. Bo Liu
7. Limeng Qiao
8. Rameswar Panda
9. Jiangpeng He
10. David Bruggemann

Papers from Simon Vandenhende's perspective

Ordered by how strongly the researchers Simon Vandenhende 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Learning To Classify Images Without Labels (2020) — 677 citations
• Branched Multi-Task Networks: Deciding what layers to share (2019) — 163 citations
• Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack (2023) — 312 citations
• MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning (2020) — 299 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• Multi-Task Learning for Dense Prediction Tasks: A Survey (2020) — 994 citations