Jindřich Libovický

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 376 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #7,007 of 35,345 by trust here, #10,798 by citations
• Natural Language Processing — 77 citations into it; #1,081 of 5,688 by trust here, #2,045 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #6,589 of 21,575 by trust here, #13,269 by citations
• Evaluation & Benchmarks — 24 citations into it; #3,005 of 3,147 by trust here, #2,330 by citations
• Model Architectures — 17 citations into it; #720 of 2,278 by trust here, #1,601 by citations
• Multimodal Learning — 11 citations into it
• Machine Translation — 8 citations into it; #45 of 292 by trust here, #188 by citations
• Molecular Biology — 8 citations into it

Whom Jindřich Libovický trusts overall

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

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

Who trusts Jindřich Libovický overall

1. Ondrej Bojar
2. Rachel Bawden
3. Desmond Elliott
4. Marjan Ghazvininejad
5. Inigo Jauregi Unanue
6. Matīss Rikters
7. Pengcheng Yin
8. Patrick Littell
9. Li Deng
10. Zhiyong Wu

Papers from Jindřich Libovický's perspective

Ordered by how strongly the researchers Jindřich Libovický 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. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
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

• CzEng 1.6: Enlarged Czech-English Parallel Corpus with Processing Tools Dockered (2016) — 97 citations
• Multimodal Abstractive Summarization for How2 Videos (2019) — 110 citations
• End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification (2018) — 175 citations
• Deep Learning for NLP (2021) — 65 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