Krzysztof Dembczynski
Artificial Intelligence · 2007–2014
A researcher in the Science corpus: 7 papers; trusts 43 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 38 citations into it; #14,420 of 35,345 by trust here, #16,471 by citations
- Statistics and Probability — 16 citations into it
- Information Systems — 14 citations into it
- Statistical Learning Theory — 13 citations into it; #517 of 1,038 by trust here, #424 by citations
- Evaluation & Benchmarks — 12 citations into it
- Optimization & Training — 12 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
- Molecular Biology — 6 citations into it
Whom Krzysztof Dembczynski trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jerome H. Friedman
- Robert E. Schapire
- Jaka Demsar
- Grigorios Tsoumakas
- Johannes Fürnkranz
- Yoav Freund
- Leo Breiman
- William W. Cohen
- David Ruppert
- Thorsten Joachims
Who trusts Krzysztof Dembczynski overall
- Dmitry M. Malioutov
- Mohammad S. Sorower
- Marjolein Fokkema
- Jesse Read
- Min-Ling Zhang
- Clément Bénard
- Jinseok Nam
- Kush R. Varshney
- Shivani Agarwal
- Matej Balog
Papers from Krzysztof Dembczynski's perspective
Ordered by how strongly the researchers Krzysztof Dembczynski trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- On label dependence and loss minimization in multi-label classification (2012) — 330 citations
- Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty (2014) — 188 citations
- ENDER: a statistical framework for boosting decision rules (2010) — 86 citations
- Statistical Model for Rough Set Approach to Multicriteria Classification (2007) — 36 citations
- Bipartite Ranking through Minimization of Univariate Loss (2011) — 100 citations
- Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains (2010) — 506 citations
- Label Ranking Methods based on the Plackett-Luce Model (2010) — 112 citations