Łukasz Kaiser

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 50 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #35,165 of 35,345 by trust here, #13,353 by citations
• Natural Language Processing — 35 citations into it; #5,663 of 5,688 by trust here, #3,053 by citations
• Computer Vision and Pattern Recognition — 30 citations into it
• Generative Models — 13 citations into it
• Model Architectures — 13 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Łukasz Kaiser trusts overall

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

1. Geoffrey E. Hinton
2. Ruslan Salakhutdinov
3. Yoshua Bengio
4. Andriy Mnih
5. Ilya Sutskever
6. Ronald J. Williams
7. Lukasz Kaiser
8. Diederik P. Kingma
9. Oriol Vinyals
10. Alex Krizhevsky

Who trusts Łukasz Kaiser overall

1. Andros Tjandra
2. Jiatao Gu
3. Carlos Escolano
4. Wei Ping
5. Kainan Peng
6. Zachary M. Ziegler
7. Mingjie Chen
8. Jungo Kasai
9. Minlie Huang
10. Sakriani Watiasri Sakti

Papers from Łukasz Kaiser's perspective

Ordered by how strongly the researchers Łukasz Kaiser 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Fast Decoding in Sequence Models using Discrete Latent Variables (2018) — 255 citations
• Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals (2020) — 331 citations