Tijmen Tieleman

Computer Vision and Pattern Recognition · 2008–2014

A researcher in the Science corpus: 3 papers; trusts 389 researchers and is trusted by 12,894 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 44 citations into it; #138 of 21,575 by trust here, #2,836 by citations
• Artificial Intelligence — 36 citations into it; #63 of 35,345 by trust here, #3,745 by citations
• Probabilistic & Bayesian Methods — 35 citations into it
• Optimization & Training — 19 citations into it; #66 of 2,601 by trust here, #359 by citations
• Representation & Self-Supervised Learning — 17 citations into it
• Generative Models — 11 citations into it; #678 of 1,956 by trust here, #1,715 by citations
• Model Architectures — 11 citations into it
• Media Technology — 7 citations into it

Whom Tijmen Tieleman trusts overall

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

1. Geoffrey E. Hinton
2. Radford M. Neal
3. Yoshua Bengio
4. Paul Smolensky
5. Ruslan Salakhutdinov
6. Yann LeCun
7. Alex Krizhevsky
8. Ilya Sutskever
9. Hugo Larochelle
10. Max Welling

Who trusts Tijmen Tieleman overall

1. Yoshua Bengio
2. Ruslan Salakhutdinov
3. Geoffrey E. Hinton
4. Aaron C. Courville
5. Kyunghyun Cho
6. Max Welling
7. Sergey Levine
8. Yilun Du
9. Trevor Darrell
10. Ian J. Goodfellow

Papers from Tijmen Tieleman's perspective

Ordered by how strongly the researchers Tijmen Tieleman trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Training restricted Boltzmann machines using approximations to the likelihood gradient (2008) — 1,066 citations
• Using fast weights to improve persistent contrastive divergence (2009) — 320 citations
• Optimizing Neural Networks that Generate Iimages (2014) — 41 citations