Stefan Lessmann

Artificial Intelligence · 2008–2020

A researcher in the Science corpus: 3 papers; trusts 68 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #13,036 of 35,345 by trust here, #10,430 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Information Systems — 24 citations into it; #2,722 of 8,771 by trust here, #3,098 by citations
• Accounting — 21 citations into it; #7,493 of 8,970 by trust here, #3,887 by citations
• Evaluation & Benchmarks — 16 citations into it
• Generative Models — 16 citations into it
• Finance — 13 citations into it
• Statistics and Probability — 13 citations into it

Whom Stefan Lessmann trusts overall

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

1. Jaka Demsar
2. Leo Breiman
3. David J. Hand
4. Tom Fawcett
5. Bart Baesens
6. Yoav Freund
7. Mark A. Hall
8. Diederik P. Kingma
9. Ian J. Goodfellow
10. Lyn C. Thomas

Who trusts Stefan Lessmann overall

1. Chakkrit Kla Tantithamthavorn
2. Kush R. Varshney
3. Ruchika Malhotra
4. David Martens
5. Ahmed E. Hassan
6. Shane McIntosh
7. Marco D'Ambros
8. Georgios Gousios
9. Francisco Louzada
10. Carlos Serrano-Cinca

Papers from Stefan Lessmann's perspective

Ordered by how strongly the researchers Stefan Lessmann 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning (2020) — 315 citations
• Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015) — 1,486 citations
• Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings (2008) — 1,224 citations