Jeremy Reizenstein

Computer Vision and Pattern Recognition · 2019–2024

A researcher in the Science corpus: 5 papers; trusts 399 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 157 citations into it; #12,535 of 21,575 by trust here, #6,737 by citations
• Artificial Intelligence — 104 citations into it
• Computational Mechanics — 76 citations into it; #8,528 of 10,001 by trust here, #2,309 by citations
• 3D Vision & Pose — 71 citations into it; #472 of 689 by trust here, #261 by citations
• Computer Graphics and Computer-Aided Design — 63 citations into it
• Generative Models — 25 citations into it
• Aerospace Engineering — 19 citations into it
• Model Architectures — 16 citations into it

Whom Jeremy Reizenstein trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Ross B. Girshick
6. Diederik P. Kingma
7. Yoshua Bengio
8. Yann LeCun
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Jeremy Reizenstein overall

1. Chaozheng Wu
2. Vincent Leroy
3. Christian Rupprecht
4. Donny Y. Chen
5. Stanislaw Szymanowicz
6. Luke Melas-Kyriazi
7. Vincent Sitzmann
8. Wangbo Yu
9. Justin Johnson
10. Andrea Vedaldi

Papers from Jeremy Reizenstein's perspective

Ordered by how strongly the researchers Jeremy Reizenstein trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) — 17,670 citations
• Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction (2021) — 744 citations
• Accelerating 3D deep learning with PyTorch3D (2019) — 1,177 citations
• Unsupervised Learning of 3D Object Categories from Videos in the Wild (2021) — 81 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations