Jerzy Stefanowski

Artificial Intelligence · 2001–2017

A researcher in the Science corpus: 7 papers; trusts 57 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #6,640 of 35,345 by trust here, #9,557 by citations
• Information Systems — 29 citations into it
• Management Science and Operations Research — 18 citations into it
• Signal Processing — 18 citations into it
• Statistical and Nonlinear Physics — 17 citations into it
• Electrical and Electronic Engineering — 14 citations into it
• Computer Networks and Communications — 12 citations into it; #2,510 of 9,886 by trust here, #5,733 by citations
• Evaluation & Benchmarks — 12 citations into it

Whom Jerzy Stefanowski trusts overall

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

1. Ludmila I. Kuncheva
2. Jaka Demsar
3. Albert Bifet
4. Nitesh V. Chawla
5. João Gama
6. Nikunj C. Oza
7. J. Kolter
8. Jorma Laurikkala
9. G. Holmes
10. W. Street

Who trusts Jerzy Stefanowski overall

1. Tyler L. Hayes
2. Qi Dong
3. Alexander Rainer Tassilo Gepperth
4. Chen Huang
5. Alberto Fernández
6. K. Ruwani
7. Osamah A. Mahdi
8. Bartosz Krawczyk
9. José A. Sáez
10. Gregory Ditzler

Papers from Jerzy Stefanowski's perspective

Ordered by how strongly the researchers Jerzy Stefanowski 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
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

• Ensemble learning for data stream analysis: A survey (2017) — 990 citations
• SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering (2015) — 583 citations
• Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm (2014) — 440 citations
• Open challenges for data stream mining research (2014) — 310 citations
• Local neighbourhood extension of SMOTE for mining imbalanced data (2011) — 256 citations
• Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition (2004) — 334 citations
• Incomplete Information Tables and Rough Classification (2001) — 364 citations