Luis Villaseñor Pineda

Computer Vision and Pattern Recognition · with Drozdzal · 2019

A researcher in the Science corpus: 3 papers; trusts 413 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 107 citations into it
• Artificial Intelligence — 70 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Model Architectures — 14 citations into it
• Reinforcement Learning — 13 citations into it
• Natural Language Processing — 12 citations into it
• Multimodal Learning — 10 citations into it
• Media Technology — 9 citations into it

Whom Luis Villaseñor Pineda trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Karen Simonyan
6. Ross B. Girshick
7. Yoshua Bengio
8. Yann LeCun
9. Ilya Sutskever
10. Andrew Zisserman

Papers from Luis Villaseñor Pineda's perspective

Ordered by how strongly the researchers Luis Villaseñor Pineda 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• Learning Causal State Representations of Partially Observable Environments (2019) — 60 citations
• On the Evaluation of Conditional GANs (2019) — 51 citations
• Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019) — 15 citations