Maria Bauzá

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 50 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 47 citations into it; #20,280 of 21,575 by trust here, #17,258 by citations
• Artificial Intelligence — 44 citations into it; #33,002 of 35,345 by trust here, #26,682 by citations
• Control and Systems Engineering — 29 citations into it; #5,015 of 8,257 by trust here, #1,122 by citations
• Biomedical Engineering — 15 citations into it; #2,096 of 10,734 by trust here, #3,269 by citations
• Graph Learning — 13 citations into it
• Robotics & Control — 7 citations into it; #412 of 609 by trust here, #395 by citations
• Cognitive Neuroscience — 5 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Maria Bauzá trusts overall

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

1. Matthew Thomas Mason
2. Quoc V. Le
3. Alberto Rodriguez
4. Miguel L'azaro-Gredilla
5. Francois Robert Hogan
6. Geoffrey E. Hinton
7. Robert Paolini
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Maria Bauzá overall

1. Andy Zeng
2. Samuel Pfrommer
3. Kuan-Ting Yu
4. Alberto Rodriguez
5. Oliver Kroemer
6. Mathew Halm
7. Shuhao Cao
8. Ferran Alet
9. Ajay Mandlekar
10. Scott Niekum

Papers from Maria Bauzá's perspective

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

Their papers

• Graph Element Networks: adaptive, structured computation and memory (2019) — 91 citations
• More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing (2016) — 199 citations
• Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching (2017) — 695 citations
• A probabilistic data-driven model for planar pushing (2017) — 113 citations