Jure Zbontar

Computer Vision and Pattern Recognition · 2013–2021

A researcher in the Science corpus: 7 papers; trusts 434 researchers and is trusted by 1,146 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 96 citations into it; #1,263 of 21,575 by trust here, #4,372 by citations
• Artificial Intelligence — 38 citations into it; #19,405 of 35,345 by trust here, #9,369 by citations
• Representation & Self-Supervised Learning — 21 citations into it; #1,979 of 3,145 by trust here, #1,220 by citations
• Evaluation & Benchmarks — 19 citations into it
• Media Technology — 19 citations into it; #1,785 of 3,621 by trust here, #544 by citations
• Aerospace Engineering — 9 citations into it
• ML Systems & Hardware — 6 citations into it
• Model Architectures — 6 citations into it; #330 of 2,278 by trust here, #969 by citations

Whom Jure Zbontar trusts overall

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

1. Daniel Scharstein
2. Heiko Hirschmüller
3. Kaiming He
4. Alex Krizhevsky
5. Yann LeCun
6. Geoffrey E. Hinton
7. Ross B. Girshick
8. Yoshua Bengio
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Jure Zbontar overall

1. Fatma Güney
2. David Gadot
3. Andreas Geiger
4. Wenjie Luo
5. Ran Tao
6. Alex Kendall
7. Hamid Laga
8. Rowel O. Atienza
9. Alexander Gerhard Schwing
10. Sunok Kim

Papers from Jure Zbontar's perspective

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

Their papers

• Computing the stereo matching cost with a convolutional neural network (2014) — 824 citations
• Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches (2015) — 1,469 citations
• Barlow Twins: Self-Supervised Learning via Redundancy Reduction (2021) — 3,100 citations
• fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. (2020) — 514 citations
• End-to-End Variational Networks for Accelerated MRI Reconstruction (2020) — 436 citations
• fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018) — 1,072 citations
• Orange: data mining toolbox in python (2013) — 2,143 citations