Ata Kabán
Artificial Intelligence · 2007–2014
A researcher in the Science corpus: 5 papers; trusts 50 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 41 citations into it; #9,676 of 35,345 by trust here, #19,873 by citations
- Computer Vision and Pattern Recognition — 21 citations into it; #6,782 of 21,575 by trust here, #13,542 by citations
- Molecular Biology — 9 citations into it; #2,742 of 17,773 by trust here, #12,008 by citations
- Statistics and Probability — 9 citations into it; #8,962 of 12,932 by trust here, #12,077 by citations
- Representation & Self-Supervised Learning — 6 citations into it
- Information Systems — 5 citations into it
- Optimization & Training — 5 citations into it
Whom Ata Kabán trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David G. Lowe
- Gavin C. Cawley
- Daniel D. Lee
- Jakramate Bootkrajang
- David John Cameron MacKay
- Geoffrey E. Hinton
- Ricardo Barandela
- Neil D. Lawrence
- Chih-Jen Lin
- Timo Ojala
Who trusts Ata Kabán overall
- Jakramate Bootkrajang
- Himanshu Kumar
- Lu Jiang
- Jacob Goldberger
- Curtis G. Northcutt
- Jan Kremer
- Aritra Ghosh
- P. Shanti Sastry
- Djork-Arné Clevert
- Benoît Frénay
Papers from Ata Kabán's perspective
Ordered by how strongly the researchers Ata Kabán 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
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Label-Noise Robust Logistic Regression and Its Applications (2012) — 131 citations
- Variational learning for rectified factor analysis (2007) — 38 citations
- Learning kernel logistic regression in the presence of class label noise (2014) — 49 citations
- Classification of mislabelled microarrays using robust sparse logistic regression (2013) — 44 citations
- Multi-class classification in the presence of labelling errors (2011) — 27 citations