Olga Vechtomova

Artificial Intelligence · 2008–2020

A researcher in the Science corpus: 7 papers; trusts 280 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 201 citations into it; #19,009 of 35,345 by trust here, #5,317 by citations
• Natural Language Processing — 162 citations into it
• Computer Vision and Pattern Recognition — 103 citations into it; #20,864 of 21,575 by trust here, #6,206 by citations
• Generative Models — 36 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Model Architectures — 17 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Olga Vechtomova trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Yann LeCun
6. Alex Krizhevsky
7. Jimmy Ba
8. Alex Graves
9. Tomas Mikolov
10. Oriol Vinyals

Who trusts Olga Vechtomova overall

1. Jimmy J. Lin

Papers from Olga Vechtomova's perspective

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

Their papers

• Distilling Task-Specific Knowledge from BERT into Simple Neural Networks (2019) — 459 citations
• Disentangled Representation Learning for Non-Parallel Text Style Transfer (2018) — 332 citations
• Novelty and diversity in information retrieval evaluation (2008) — 1,088 citations
• Generating Sentences from Disentangled Syntactic and Semantic Spaces (2019) — 122 citations
• Deep Learning for Text Style Transfer: A Survey (2020) — 303 citations
• Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (2018) — 23 citations
• Queuing Systems (2018) — 319 citations