Jeff Johnson

Computer Vision and Pattern Recognition · with Douze · 2014–2024

A researcher in the Science corpus: 4 papers; trusts 342 researchers and is trusted by 475 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 88 citations into it; #1,971 of 21,575 by trust here, #2,027 by citations
• Artificial Intelligence — 58 citations into it; #12,360 of 35,345 by trust here, #30,347 by citations
• Information Retrieval & Search — 32 citations into it
• Efficiency & Compression — 17 citations into it; #935 of 1,264 by trust here, #788 by citations
• Signal Processing — 17 citations into it
• ML Systems & Hardware — 16 citations into it; #125 of 575 by trust here, #312 by citations
• Aerospace Engineering — 13 citations into it
• Hardware and Architecture — 12 citations into it

Whom Jeff Johnson trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Herv'e J'egou
4. Geoffrey E. Hinton
5. Solomon W. Golomb
6. Ilya Sutskever
7. X. Zhang
8. Shaoqing Ren
9. Yann LeCun
10. Yoshua Bengio

Who trusts Jeff Johnson overall

1. Luyu Gao
2. Omar Khattab
3. Jimmy J. Lin
4. Gautier Izacard
5. Patrick Lewis
6. Angela Fan
7. Sheng-Chieh Lin
8. Mohit Bansal
9. Fabio Petroni
10. Bill Yuchen Lin

Papers from Jeff Johnson's perspective

Ordered by how strongly the researchers Jeff Johnson 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. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Billion-Scale Similarity Search with GPUs (2017) — 5,541 citations
• Fast Convolutional Nets With fbfft: A GPU Performance Evaluation (2014) — 362 citations
• Rethinking floating point for deep learning (2018) — 155 citations
• The Faiss Library (2024) — 768 citations