Mikel D. Rodriguez
Computer Vision and Pattern Recognition · 2008–2016
A researcher in the Science corpus: 4 papers; trusts 508 researchers and is trusted by 1,787 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 94 citations into it; #955 of 21,575 by trust here, #2,304 by citations
- Artificial Intelligence — 31 citations into it; #19,696 of 35,345 by trust here, #19,018 by citations
- Aerospace Engineering — 12 citations into it
- Biomedical Engineering — 8 citations into it; #259 of 10,734 by trust here, #1,815 by citations
- Evaluation & Benchmarks — 5 citations into it
- Representation & Self-Supervised Learning — 5 citations into it; #1,150 of 3,145 by trust here, #982 by citations
Whom Mikel D. Rodriguez trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ross B. Girshick
- Geoffrey E. Hinton
- Kaiming He
- Jianguo Zhang
- Andrew Zisserman
- Alex Krizhevsky
- Pedro F. Felzenszwalb
- Alexei A. Efros
- Ron Kohavi
- Antonio Torralba
Who trusts Mikel D. Rodriguez overall
- Limin Wang
- Wenguan Wang
- Hossein Rahmani
- Jitendra Malik
- Vishwanath A. Sindagi
- Mubarak Shah
- Cordelia Schmid
- Haroon Idrees
- Yui Man Lui
- Luc Van Gool
Papers from Mikel D. Rodriguez's perspective
Ordered by how strongly the researchers Mikel D. Rodriguez trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition (2008) — 1,386 citations
- Learning a Predictable and Generative Vector Representation for Objects (2016) — 747 citations
- Representing Videos Using Mid-level Discriminative Patches (2013) — 155 citations
- Density-aware person detection and tracking in crowds (2011) — 378 citations