David P. Helmbold

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 3 papers; trusts 101 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #10,209 of 35,345 by trust here, #18,586 by citations
• Optimization & Training — 28 citations into it; #1,414 of 2,601 by trust here, #1,866 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Model Architectures — 11 citations into it
• Statistical Learning Theory — 8 citations into it
• Computational Mechanics — 6 citations into it; #2,400 of 10,001 by trust here, #4,039 by citations
• Natural Language Processing — 6 citations into it
• Scaling & Learning Theory — 6 citations into it

Whom David P. Helmbold trusts overall

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

1. Geoffrey E. Hinton
2. J. Urgen Schmidhuber
3. Kaiming He
4. Yoshua Bengio
5. Alex Krizhevsky
6. Stefan Wager
7. Tong Zhang
8. Yann LeCun
9. Ilya Sutskever
10. Pierre Baldi

Who trusts David P. Helmbold overall

1. Nicholas G. Polson
2. Stefan Wager
3. Hongchang Gao
4. Linjun Zhang
5. Ohad Shamir
6. Simon Shaolei Du
7. Philip M. Long
8. Seyed Iman Mirzadeh
9. Colin Wei
10. Vadim O. Sokolov

Papers from David P. Helmbold's perspective

Ordered by how strongly the researchers David P. Helmbold 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. 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. 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

• Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks (2018) — 125 citations
• On the inductive bias of dropout (2014) — 74 citations
• Surprising properties of dropout in deep networks (2016) — 37 citations