Igor Labutov

Natural Language Processing · 2013–2019

A researcher in the Science corpus: 8 papers; trusts 322 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 122 citations into it; #2,330 of 5,688 by trust here, #3,421 by citations
• Artificial Intelligence — 121 citations into it; #6,542 of 35,345 by trust here, #7,797 by citations
• Computer Vision and Pattern Recognition — 44 citations into it; #13,532 of 21,575 by trust here, #17,258 by citations
• Representation & Self-Supervised Learning — 34 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Information Systems — 12 citations into it
• Information Retrieval & Search — 11 citations into it
• Reinforcement Learning — 10 citations into it

Whom Igor Labutov trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Jason Aaron Edward Weston
5. Ronan Collobert
6. Alex Krizhevsky
7. Percy Liang
8. Ilya Sutskever
9. Yann LeCun
10. Oriol Vinyals

Who trusts Igor Labutov overall

1. Duyu Tang
2. Sudha Rao
3. Xinya Du
4. Peter Hase
5. Siva Reddy
6. Nan Duan
7. Xingdi Yuan
8. Hao Peng
9. Mohit Bansal
10. Sandeep Subramanian

Papers from Igor Labutov's perspective

Ordered by how strongly the researchers Igor Labutov 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Joint Concept Learning and Semantic Parsing from Natural Language Explanations (2017) — 96 citations
• Zero-shot Learning of Classifiers from Natural Language Quantification (2018) — 59 citations
• Deep Questions without Deep Understanding (2015) — 135 citations
• Evaluation methods for unsupervised word embeddings (2015) — 583 citations
• Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds (2018) — 14 citations
• Learning to Learn Semantic Parsers from Natural Language Supervision (2019) — 16 citations
• Learning Classifiers from Declarative Language (2018)
• Re-embedding words (2013) — 114 citations