Daniel Keysers

Computer Vision and Pattern Recognition · Information retrieval (Boston) · 2004–2021

A researcher in the Science corpus: 10 papers; trusts 559 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 273 citations into it; #12,133 of 21,575 by trust here, #2,322 by citations
• Artificial Intelligence — 225 citations into it; #15,768 of 35,345 by trust here, #3,192 by citations
• Representation & Self-Supervised Learning — 85 citations into it; #2,867 of 3,145 by trust here, #1,321 by citations
• Model Architectures — 63 citations into it
• Evaluation & Benchmarks — 48 citations into it
• Optimization & Training — 28 citations into it
• Media Technology — 17 citations into it
• Aerospace Engineering — 9 citations into it

Whom Daniel Keysers trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. David G. Lowe
5. Kaiming He
6. Diederik P. Kingma
7. Yann LeCun
8. Ilya Sutskever
9. Sepp Hochreiter
10. Ross B. Girshick

Who trusts Daniel Keysers overall

1. Christian Beecks
2. Najoung Kim
3. Jonathan Herzig
4. Peter Shaw
5. Tal Linzen
6. R. Thomas McCoy
7. Neerad Phansalkar
8. Belinda Z. Li
9. Qian Liu
10. Andrew Drozdov

Papers from Daniel Keysers's perspective

Ordered by how strongly the researchers Daniel Keysers 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• MLP-Mixer: An all-MLP Architecture for Vision (2021) — 3,705 citations
• The 2005 PASCAL Visual Object Classes Challenge (2005) — 525 citations
• LiT: Zero-Shot Transfer with Locked-image text Tuning (2021) — 757 citations
• Scaling Vision with Sparse Mixture of Experts (2021) — 1,110 citations
• Measuring Compositional Generalization: A Comprehensive Method on Realistic Data (2019) — 421 citations
• Scalable Transfer Learning with Expert Models (2020) — 72 citations
• Efficient implementation of local adaptive thresholding techniques using integral images (2008) — 361 citations
• Deep Ensembles for Low-Data Transfer Learning (2020) — 27 citations
• Content-based Image Retrieval in Medical Applications (2004) — 365 citations
• Features for image retrieval: an experimental comparison (2008) — 654 citations