Michal Grochowski

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 305 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 63 citations into it; #6,015 of 21,575 by trust here, #10,677 by citations
• Artificial Intelligence — 34 citations into it; #8,402 of 35,345 by trust here, #15,542 by citations
• Generative Models — 21 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 11 citations into it
• Media Technology — 8 citations into it
• Cognitive Neuroscience — 7 citations into it
• Computer Graphics and Computer-Aided Design — 7 citations into it

Whom Michal Grochowski trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Alex Krizhevsky
4. Sergey Ioffe
5. Yann LeCun
6. Kaiming He
7. Agnieszka Mikołajczyk
8. Yoshua Bengio
9. Christian Szegedy
10. Roman Novak

Who trusts Michal Grochowski overall

1. Agnieszka Mikołajczyk
2. Evgenia Rusak
3. Arkadiusz Kwasigroch
4. Cherry Khosla
5. Khaled Alomar
6. Muhammet Fatih Aslan
7. Guillermo Alonso Iglesias
8. Steffen Schneider
9. Abdul Waheed
10. Lukas Schott

Papers from Michal Grochowski's perspective

Ordered by how strongly the researchers Michal Grochowski 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. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Data augmentation for improving deep learning in image classification problem (2018) — 1,460 citations
• Deep neural network architecture search using network morphism (2019) — 8 citations
• Style transfer-based image synthesis as an efficient regularization technique in deep learning (2019) — 23 citations
• Deep neural networks approach to skin lesions classification — A comparative analysis (2017) — 56 citations