Julieta Martinez

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 414 researchers and is trusted by 114 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 91 citations into it; #3,096 of 21,575 by trust here, #5,620 by citations
• Artificial Intelligence — 36 citations into it
• Human-Computer Interaction — 19 citations into it; #417 of 2,253 by trust here, #389 by citations
• Biomedical Engineering — 15 citations into it
• Optimization & Training — 12 citations into it
• Model Architectures — 11 citations into it
• Control and Systems Engineering — 10 citations into it; #4,385 of 8,257 by trust here, #4,722 by citations
• Representation & Self-Supervised Learning — 6 citations into it

Whom Julieta Martinez trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ilya Sutskever
7. David G. Lowe
8. Yann LeCun
9. Ross B. Girshick
10. Alex Graves

Who trusts Julieta Martinez overall

1. Georgios Pavlakos
2. Andrei Zanfir
3. Wei Mao
4. Dario Pavllo
5. Michael J. Black
6. Ye Yuan
7. Mir Rayat Imtiaz Hossain
8. Xiao Sun
9. Gyeongsik Moon
10. Jiefeng Li

Papers from Julieta Martinez's perspective

Ordered by how strongly the researchers Julieta Martinez 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. Gradient-based learning applied to document recognition
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. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Simple Yet Effective Baseline for 3d Human Pose Estimation (2017) — 1,518 citations
• On Human Motion Prediction Using Recurrent Neural Networks (2017) — 1,057 citations
• 3D Pose from Motion for Cross-View Action Recognition via Non-linear Circulant Temporal Encoding (2014) — 124 citations
• Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search (2014)