Jonathon S. Hare
Computer Vision and Pattern Recognition · 2017–2020
A researcher in the Science corpus: 10 papers; trusts 735 researchers and is trusted by 46 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 178 citations into it; #4,853 of 21,575 by trust here, #7,027 by citations
- Artificial Intelligence — 170 citations into it; #6,509 of 35,345 by trust here, #6,266 by citations
- Natural Language Processing — 58 citations into it; #4,782 of 5,688 by trust here, #2,333 by citations
- Media Technology — 55 citations into it
- Model Architectures — 50 citations into it; #899 of 2,278 by trust here, #1,927 by citations
- Optimization & Training — 24 citations into it; #2,266 of 2,601 by trust here, #2,313 by citations
- Atmospheric Science — 20 citations into it
- Ecology — 19 citations into it
Whom Jonathon S. Hare trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Yann LeCun
- Karen Simonyan
- Oriol Vinyals
- Andrew Zisserman
Who trusts Jonathon S. Hare overall
- Yan Zhang
- David W. Zhang
- Adam R. Kosiorek
- Zhou Yu
- Carl Yang
- Qian Huang
- Marco Tulio Ribeiro
- Gertjan J. Burghouts
- Swarnadeep Saha
- Dalu Guo
Papers from Jonathon S. Hare's perspective
Ordered by how strongly the researchers Jonathon S. Hare 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Learning to Count Objects in Natural Images for Visual Question Answering (2018) — 224 citations
- T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples (2018) — 381 citations
- Deep Set Prediction Networks (2019) — 130 citations
- FSPool: Learning Set Representations with Featurewise Sort Pooling (2019) — 94 citations
- FMix: Enhancing Mixed Sample Data Augmentation (2020) — 183 citations
- Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples (2017) — 70 citations
- Joint Deep Learning for land cover and land use classification (2019) — 458 citations
- Deep Cascade Learning (2018) — 126 citations
- An object-based convolutional neural network (OCNN) for urban land use classification (2018) — 423 citations
- A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification (2017) — 391 citations