Bartosz Krawczyk

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 6 papers; trusts 181 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 145 citations into it; #8,884 of 35,345 by trust here, #9,369 by citations
• Computer Vision and Pattern Recognition — 39 citations into it
• Information Systems — 27 citations into it
• Electrical and Electronic Engineering — 22 citations into it; #2,214 of 16,539 by trust here, #6,854 by citations
• Management Science and Operations Research — 16 citations into it
• Optimization & Training — 16 citations into it
• Generative Models — 15 citations into it
• Signal Processing — 14 citations into it

Whom Bartosz Krawczyk trusts overall

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

1. Ludmila I. Kuncheva
2. Haibo He
3. Albert Bifet
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Nitesh V. Chawla
8. Yanmin Sun
9. Alberto Fernández
10. Robert P. W. Duin

Who trusts Bartosz Krawczyk overall

1. Adamu Ali-Gombe
2. Harsurinder Kaur
3. Alberto Fernández
4. Eyad Elyan
5. Husanbir Singh Pannu
6. Salvador Garcia
7. Haoyue Liu
8. Francisco Herrera
9. Avleen Kaur Malhi
10. Damien Dablain

Papers from Bartosz Krawczyk's perspective

Ordered by how strongly the researchers Bartosz Krawczyk 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning from imbalanced data: open challenges and future directions (2016) — 2,330 citations
• Cost-sensitive decision tree ensembles for effective imbalanced classification (2014) — 361 citations
• DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data (2021) — 502 citations
• Learning from Imbalanced Data Sets (2018) — 1,073 citations
• A survey on data preprocessing for data stream mining: Current status and future directions (2017) — 457 citations
• Ensemble learning for data stream analysis: A survey (2017) — 990 citations