Guy Lev

Computer Vision and Pattern Recognition · 2014–2015

A researcher in the Science corpus: 4 papers; trusts 759 researchers and is trusted by 364 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 127 citations into it; #2,132 of 21,575 by trust here, #5,507 by citations
• Artificial Intelligence — 77 citations into it; #5,979 of 35,345 by trust here, #8,706 by citations
• Natural Language Processing — 62 citations into it; #4,620 of 5,688 by trust here, #5,412 by citations
• Multimodal Learning — 35 citations into it; #341 of 1,211 by trust here, #671 by citations
• Representation & Self-Supervised Learning — 22 citations into it; #1,059 of 3,145 by trust here, #1,493 by citations
• Model Architectures — 16 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Media Technology — 13 citations into it; #2,294 of 3,621 by trust here, #1,859 by citations

Whom Guy Lev trusts overall

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

1. Karen Simonyan
2. Yann LeCun
3. Florent Perronnin
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Tomas Mikolov
7. Ilya Sutskever
8. Andrew Zisserman
9. Alex Krizhevsky
10. Richard Socher

Who trusts Guy Lev overall

1. Yan Huang
2. Antoine Miech
3. Bryan A. Plummer
4. Zhedong Zheng
5. Jianfeng Dong
6. Liang Wang
7. Svetlana Lazebnik
8. Liwei Wang
9. Liang Zheng
10. Chiara Plizzari

Papers from Guy Lev's perspective

Ordered by how strongly the researchers Guy Lev 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Associating neural word embeddings with deep image representations using Fisher Vectors (2015) — 302 citations
• RNN Fisher Vectors for Action Recognition and Image Annotation (2015) — 172 citations
• Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation (2014) — 103 citations
• In Defense of Word Embedding for Generic Text Representation (2015) — 41 citations