Pierre L. Dognin

Computer Vision and Pattern Recognition · 2012–2021

A researcher in the Science corpus: 5 papers; trusts 194 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 45 citations into it
• Artificial Intelligence — 41 citations into it; #21,866 of 35,345 by trust here, #30,347 by citations
• Generative Models — 28 citations into it
• Optimization & Training — 17 citations into it
• Signal Processing — 9 citations into it; #4,471 of 7,572 by trust here, #6,591 by citations
• Representation & Self-Supervised Learning — 8 citations into it
• Reinforcement Learning — 6 citations into it
• Applied Mathematics — 5 citations into it

Whom Pierre L. Dognin trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. James Martens
4. Ronald J. Williams
5. Yann LeCun
6. L. Eon Bottou
7. Alex Krizhevsky
8. Yoshua Bengio
9. Ilya Sutskever
10. Ryan Kiros

Who trusts Pierre L. Dognin overall

1. Giulia Luise
2. Gonzalo Rios
3. Li Deng
4. Bo Li

Papers from Pierre L. Dognin's perspective

Ordered by how strongly the researchers Pierre L. Dognin 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning Implicit Generative Models by Matching Perceptual Features (2019) — 32 citations
• ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models (2021) — 30 citations
• Factorial Hidden Restricted Boltzmann Machines for noise robust speech recognition (2012) — 8 citations
• Wasserstein Barycenter Model Ensembling (2019) — 27 citations
• Combining stochastic average gradient and Hessian-free optimization for sequence training of deep neural networks (2013) — 8 citations