Ondrej Bajgar
Artificial Intelligence · 2016
A researcher in the Science corpus: 3 papers; trusts 93 researchers and is trusted by 61 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 65 citations into it; #4,371 of 35,345 by trust here, #7,112 by citations
- Natural Language Processing — 55 citations into it; #1,289 of 5,688 by trust here, #1,837 by citations
- Computer Vision and Pattern Recognition — 27 citations into it; #5,505 of 21,575 by trust here, #5,890 by citations
- Evaluation & Benchmarks — 20 citations into it; #1,899 of 3,147 by trust here, #2,482 by citations
- Model Architectures — 9 citations into it
- Question Answering & Reading Comprehension — 8 citations into it
- Optimization & Training — 6 citations into it
- Statistical and Nonlinear Physics — 6 citations into it
Whom Ondrej Bajgar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Yiming Cui
- Tomas Mikolov
- Ilya Sutskever
- Jason Aaron Edward Weston
- Jimmy Ba
- Alex Krizhevsky
- Christopher D. Manning
Who trusts Ondrej Bajgar overall
- Bhuwan Dhingra
- Simon Suster
- Petros Stavropoulos
- Zhuosheng Zhang
- Yiming Cui
- Tomás Kociský
- Minghao Hu
- Yi Tay
- Danqi Chen
- Kai Sun
Papers from Ondrej Bajgar's perspective
Ordered by how strongly the researchers Ondrej Bajgar trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)