Miguel Angel Bautista

Computer Vision and Pattern Recognition · with Escalera · 2012–2018

A researcher in the Science corpus: 5 papers; trusts 512 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 85 citations into it; #7,820 of 21,575 by trust here, #7,499 by citations
• Artificial Intelligence — 47 citations into it; #11,755 of 35,345 by trust here, #16,166 by citations
• Representation & Self-Supervised Learning — 24 citations into it; #2,170 of 3,145 by trust here, #2,957 by citations
• Evaluation & Benchmarks — 13 citations into it
• Biomedical Engineering — 7 citations into it
• Human-Computer Interaction — 7 citations into it

Whom Miguel Angel Bautista trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Nitish Srivastava
4. Ross B. Girshick
5. Ilya Sutskever
6. Kaiming He
7. Pedro F. Felzenszwalb
8. Yoshua Bengio
9. Yann LeCun
10. Mark Everingham

Who trusts Miguel Angel Bautista overall

1. Xu Ji
2. Unaiza Ahsan
3. Ahmet Iscen
4. Andrea Vedaldi
5. Patrick Esser
6. Artsiom Sanakoyeu
7. Rishi Madhok
8. Adrien Bardes
9. A. Piergiovanni
10. Björn Ommer

Papers from Miguel Angel Bautista's perspective

Ordered by how strongly the researchers Miguel Angel Bautista 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• CliqueCNN: Deep Unsupervised Exemplar Learning (2016) — 116 citations
• ChaLearn Looking at People Challenge 2014: Dataset and Results (2014) — 239 citations
• Deep Unsupervised Similarity Learning Using Partially Ordered Sets (2017) — 28 citations
• Beyond One-hot Encoding: lower dimensional target embedding (2018) — 441 citations
• BoVDW: Bag-of-Visual-and-Depth-Words for gesture recognition (2012) — 41 citations