Daniel Holden

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 64 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 39 citations into it; #4,385 of 21,575 by trust here, #7,499 by citations
• Control and Systems Engineering — 19 citations into it; #535 of 8,257 by trust here, #1,064 by citations
• Artificial Intelligence — 17 citations into it
• Optimization & Training — 5 citations into it

Whom Daniel Holden trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Ilya Sutskever
5. Jinxiang Chai
6. Diederik P. Kingma
7. Pascal Vincent
8. Yann LeCun
9. J. Urgen Schmidhuber
10. Sepp Hochreiter

Who trusts Daniel Holden overall

1. Dario Pavllo
2. He Zhang
3. Sebastian Starke
4. Taku Komura
5. Hung Yu Ling
6. Michael J. Black
7. Emre Aksan
8. Mathis Petrovich
9. Chuan Guo
10. Guy Tevet

Papers from Daniel Holden's perspective

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

Their papers

• Phase-functioned neural networks for character control (2017) — 668 citations
• A Deep Learning Framework for Character Motion Synthesis and Editing (2016) — 738 citations
• Learning motion manifolds with convolutional autoencoders (2015) — 311 citations