Ruben Villegas

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 7 papers; trusts 557 researchers and is trusted by 376 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 173 citations into it; #2,137 of 21,575 by trust here, #2,081 by citations
• Artificial Intelligence — 104 citations into it; #11,865 of 35,345 by trust here, #5,359 by citations
• Generative Models — 74 citations into it; #1,440 of 1,956 by trust here, #807 by citations
• Reinforcement Learning — 42 citations into it; #1,162 of 2,242 by trust here, #663 by citations
• Representation & Self-Supervised Learning — 21 citations into it
• Model Architectures — 18 citations into it
• Control and Systems Engineering — 16 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it

Whom Ruben Villegas trusts overall

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

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

Who trusts Ruben Villegas overall

1. Yunbo Wang
2. Vincent Le Guen
3. Longlong Jing
4. Danijar Hafner
5. Andreas Blattmann
6. Sergiu Oprea
7. Yijun Li
8. Eiji Watanabe
9. Nicolas Thomé
10. Yingli Tian

Papers from Ruben Villegas's perspective

Ordered by how strongly the researchers Ruben Villegas 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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

• Decomposing Motion and Content for Natural Video Sequence Prediction (2017) — 639 citations
• Learning Latent Dynamics for Planning from Pixels (2018) — 1,944 citations
• Learning to Generate Long-term Future via Hierarchical Prediction (2017) — 400 citations
• Phenaki: Variable Length Video Generation From Open Domain Textual Description (2022) — 572 citations
• Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction (2015) — 221 citations
• Unsupervised Learning of Object Structure and Dynamics from Videos (2019) — 162 citations
• High Fidelity Video Prediction with Large Neural Nets (2019) — 3 citations