Jindřich Helcl

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 275 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #8,052 of 35,345 by trust here, #18,186 by citations
• Natural Language Processing — 52 citations into it; #1,130 of 5,688 by trust here, #2,567 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #6,590 of 21,575 by trust here, #15,955 by citations
• Model Architectures — 13 citations into it; #719 of 2,278 by trust here, #1,601 by citations
• Evaluation & Benchmarks — 12 citations into it
• Machine Translation — 7 citations into it; #46 of 292 by trust here, #188 by citations
• Multimodal Learning — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Jindřich Helcl trusts overall

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

1. Kyunghyun Cho
2. Oriol Vinyals
3. Yoshua Bengio
4. Dzmitry Bahdanau
5. Rico Sennrich
6. Alex Graves
7. Geoffrey E. Hinton
8. Matthew D. Zeiler
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Jindřich Helcl overall

1. Ondrej Bojar
2. Matīss Rikters
3. Jindřich Libovický
4. Franck Burlot
5. Ondřej Cífka
6. Dusan Varis
7. Julia Kreutzer
8. Jasmijn Bastings
9. François Yvon
10. Rachel Bawden

Papers from Jindřich Helcl's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification (2018) — 175 citations
• Neural Monkey: An Open-source Tool for Sequence Learning (2017) — 71 citations
• Attention Strategies for Multi-Source Sequence-to-Sequence Learning (2017) — 192 citations