Bryan Eikema

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 193 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #20,391 of 35,345 by trust here, #25,809 by citations
• Natural Language Processing — 41 citations into it; #3,457 of 5,688 by trust here, #4,682 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Statistics and Probability — 14 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Model Architectures — 9 citations into it
• Large Language Models — 5 citations into it

Whom Bryan Eikema trusts overall

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

1. Alex Graves
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. David M. Blei
8. Tetiana Parshakova
9. Alex Krizhevsky
10. L. Eon Bottou

Who trusts Bryan Eikema overall

1. Clara Meister
2. Dongyoung Go
3. Tomasz Korbak
4. Freda Shi
5. Ryan Cotterell
6. Markus Freitag
7. Daniel Fried
8. Subhajit Naskar
9. Germán Kruszewski
10. Ricardo Rei

Papers from Bryan Eikema's perspective

Ordered by how strongly the researchers Bryan Eikema trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation (2020) — 169 citations
• An approximate sampler for energy-based models with divergence diagnostics (2022) — 11 citations