Daniel Munoz

Computer Vision and Pattern Recognition · 2009–2014

A researcher in the Science corpus: 4 papers; trusts 438 researchers and is trusted by 993 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 52 citations into it; #1,368 of 21,575 by trust here, #4,245 by citations
• Artificial Intelligence — 23 citations into it; #5,990 of 35,345 by trust here, #9,870 by citations
• Natural Language Processing — 10 citations into it; #1,003 of 5,688 by trust here, #2,011 by citations
• Model Architectures — 6 citations into it; #595 of 2,278 by trust here, #1,352 by citations
• Reinforcement Learning — 6 citations into it

Whom Daniel Munoz trusts overall

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

1. Nathan D. Ratliff
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Martin J. Wainwright
5. B. Taskar
6. Martial Hebert
7. Yann LeCun
8. Alex Krizhevsky
9. Sanjiv Kumar
10. Kaiming He

Who trusts Daniel Munoz overall

1. Nathan D. Ratliff
2. Stéphane Ross
3. Jens Behley
4. Varun Ramakrishna
5. Andrew Bagnell
6. Shih-En Wei
7. Richard Zhang
8. Liang-Chieh Chen
9. J. Bagnell
10. Min Sun

Papers from Daniel Munoz's perspective

Ordered by how strongly the researchers Daniel Munoz 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Stacked Hierarchical Labeling (2010) — 249 citations
• Learning message-passing inference machines for structured prediction (2011) — 122 citations
• Pose Machines: Articulated Pose Estimation via Inference Machines (2014) — 263 citations
• Contextual classification with functional Max-Margin Markov Networks (2009) — 363 citations