Natalia A. Tomashenko

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #20,023 of 35,345 by trust here, #17,412 by citations
• Signal Processing — 29 citations into it; #4,139 of 7,572 by trust here, #3,309 by citations
• Speech & Audio — 26 citations into it; #633 of 1,076 by trust here, #667 by citations
• Natural Language Processing — 14 citations into it
• Representation & Self-Supervised Learning — 9 citations into it; #2,524 of 3,145 by trust here, #3,140 by citations
• Model Architectures — 7 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Natalia A. Tomashenko trusts overall

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

1. Geoffrey E. Hinton
2. Dong Yu
3. Hank Liao
4. Jean-Luc Gauvain
5. Frank Torsten Bernd Seide
6. George E. Dahl
7. Bo Li
8. Daniel Povey
9. Yoshua Bengio
10. Kaisheng Yao

Who trusts Natalia A. Tomashenko overall

1. François Hernandez
2. Sahar Ghannay
3. Tatiana Likhomanenko

Papers from Natalia A. Tomashenko's perspective

Ordered by how strongly the researchers Natalia A. Tomashenko 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation (2018) — 434 citations
• Investigating Self-Supervised Pre-Training for End-to-End Speech Translation (2020) — 40 citations
• Speaker adaptation of context dependent deep neural networks based on MAP-adaptation and GMM-derived feature processing (2014) — 27 citations