Elena Smirnova

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 53 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #11,999 of 35,345 by trust here, #23,077 by citations
• Information Systems — 21 citations into it; #2,070 of 8,771 by trust here, #5,078 by citations
• Computer Vision and Pattern Recognition — 11 citations into it
• Natural Language Processing — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Recommender Systems — 8 citations into it; #128 of 233 by trust here, #208 by citations
• Model Architectures — 7 citations into it; #1,206 of 2,278 by trust here, #2,128 by citations
• Management Science and Operations Research — 6 citations into it; #4,808 of 11,946 by trust here, #9,383 by citations

Whom Elena Smirnova trusts overall

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

1. Tomas Mikolov
2. Balázs Hidasi
3. Bartlomiej Twardowski
4. Yoshua Bengio
5. Yehuda Koren
6. Geoffrey E. Hinton
7. Ilya Sutskever
8. Ruslan Salakhutdinov
9. Kyunghyun Cho
10. Quoc V. Le

Who trusts Elena Smirnova overall

1. Shoujin Wang
2. Dawen Liang
3. Le Wu
4. Fajie Yuan
5. Xiangnan He
6. Longbing Cao
7. Rahul G. Krishnan
8. Massimo Quadrana
9. Alexandros Karatzoglou
10. Aminu Da’u

Papers from Elena Smirnova's perspective

Ordered by how strongly the researchers Elena Smirnova 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks (2017) — 173 citations
• Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation (2016) — 273 citations