Genevieve Patterson
Computer Vision and Pattern Recognition · 2012–2018
A researcher in the Science corpus: 5 papers; trusts 738 researchers and is trusted by 1,278 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 105 citations into it; #1,162 of 21,575 by trust here, #4,555 by citations
- Artificial Intelligence — 49 citations into it; #9,679 of 35,345 by trust here, #14,181 by citations
- Evaluation & Benchmarks — 23 citations into it; #1,698 of 3,147 by trust here, #2,903 by citations
- Natural Language Processing — 12 citations into it
- Media Technology — 9 citations into it
- Representation & Self-Supervised Learning — 9 citations into it
- Signal Processing — 7 citations into it
- Computer Science Applications — 6 citations into it
Whom Genevieve Patterson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Antonio Torralba
- Aude Oliva
- Jia Deng
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Devi Parikh
- Ross B. Girshick
- Yoshua Bengio
- Diederik P. Kingma
Who trusts Genevieve Patterson overall
- Bolei Zhou
- Yongqin Xian
- Mohammad Rostami
- Nikolaos Passalis
- Soravit Changpinyo
- Yuke Zhu
- Bernt Schiele
- Edgar Schönfeld
- Antonio Torralba
- Zeynep Akata
Papers from Genevieve Patterson's perspective
Ordered by how strongly the researchers Genevieve Patterson 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- SUN attribute database: Discovering, annotating, and recognizing scene attributes (2012) — 937 citations
- The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding (2014) — 424 citations
- COCO Attributes: Attributes for People, Animals, and Objects (2016) — 110 citations
- DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation (2018) — 25 citations
- Bootstrapping Fine-Grained Classifiers : Active Learning with a Crowd in the Loop (2013)