Björn Barz

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 2 papers; trusts 530 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 58 citations into it; #13,897 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 37 citations into it; #22,397 of 35,345 by trust here, #21,316 by citations
• Evaluation & Benchmarks — 15 citations into it; #2,005 of 3,147 by trust here, #2,627 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Model Architectures — 11 citations into it
• Media Technology — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Björn Barz trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Antonio Torralba
6. Yann LeCun
7. Jia Deng
8. Ilya Sutskever
9. Ross B. Girshick
10. Christian Szegedy

Who trusts Björn Barz overall

1. Nikhil Vyas
2. Eduardo Fonseca
3. Andrew Ilyas
4. Alessandro Achille
5. Thomas Liao
6. Alexandra Sasha Luccioni
7. Alexander Kolesnikov
8. Sham M. Kakade
9. Lucas Beyer
10. Xavier Favory

Papers from Björn Barz's perspective

Ordered by how strongly the researchers Björn Barz 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

• Do We Train on Test Data? Purging CIFAR of Near-Duplicates (2019) — 116 citations
• Deep Learning on Small Datasets without Pre-Training using Cosine Loss (2019) — 157 citations