Ü. Dogan
Artificial Intelligence · 2016–2017
A researcher in the Science corpus: 3 papers; trusts 49 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 67 citations into it
- Representation & Self-Supervised Learning — 36 citations into it
- Computer Vision and Pattern Recognition — 33 citations into it
- Statistical Learning Theory — 28 citations into it; #918 of 1,038 by trust here, #985 by citations
- Statistics and Probability — 17 citations into it
- Evaluation & Benchmarks — 9 citations into it
- Computational Mechanics — 8 citations into it
- Molecular Biology — 6 citations into it
Whom Ü. Dogan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ingo Steinwart
- Chih-Jen Lin
- Yoonkyung Lee
- Jason Aaron Edward Weston
- Andreas Christmann
- Corinna Cortes
- Peter L. Bartlett
- Vladimir Naumovich Vapnik
- Koby Crammer
- L. Eon Bottou
Who trusts Ü. Dogan overall
Papers from Ü. Dogan's perspective
Ordered by how strongly the researchers Ü. Dogan 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
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- 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)
Their papers
- Domain Generalization by Marginal Transfer Learning (2017) — 354 citations
- Multi-Task Learning for Contextual Bandits (2017) — 101 citations
- A Unified View on Multi-class Support Vector Classification (2016) — 110 citations