Mateusz Buda

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 423 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 99 citations into it; #6,016 of 21,575 by trust here, #6,326 by citations
• Artificial Intelligence — 85 citations into it; #7,855 of 35,345 by trust here, #7,728 by citations
• Radiology, Nuclear Medicine and Imaging — 34 citations into it; #2,240 of 3,265 by trust here, #2,257 by citations
• Model Architectures — 24 citations into it
• Biomedical Engineering — 16 citations into it
• Neurology — 14 citations into it; #926 of 1,455 by trust here, #1,382 by citations
• Optimization & Training — 12 citations into it; #900 of 2,601 by trust here, #836 by citations
• Media Technology — 8 citations into it

Whom Mateusz Buda trusts overall

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

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

Who trusts Mateusz Buda overall

1. Eden Belouadah
2. Shiori Sagawa
3. Yuzhe Yang
4. Damien Teney
5. Weihao Weng
6. Adrian Daniel Popescu
7. Chris Dongjoo Kim
8. Dávid Rozenberszki
9. Benjamin Kellenberger
10. Tianxiang Zhao

Papers from Mateusz Buda's perspective

Ordered by how strongly the researchers Mateusz Buda 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• A systematic study of the class imbalance problem in convolutional neural networks (2017) — 3,026 citations
• Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm (2019) — 339 citations
• Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018) — 523 citations