Tetiana Parshakova

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 47 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it; #9,878 of 35,345 by trust here, #20,836 by citations
• Natural Language Processing — 12 citations into it
• Reinforcement Learning — 11 citations into it; #1,241 of 2,242 by trust here, #2,063 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Model Architectures — 8 citations into it; #957 of 2,278 by trust here, #1,940 by citations
• Probabilistic & Bayesian Methods — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Tetiana Parshakova trusts overall

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

1. Yoshua Bengio
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Sepp Hochreiter
5. Yann LeCun
6. Ronald J. Williams
7. Diederik P. Kingma
8. Oriol Vinyals
9. J. Urgen Schmidhuber
10. Christian P. Robert

Who trusts Tetiana Parshakova overall

1. Tomasz Korbak
2. Bryan Eikema
3. Hady ElSahar
4. Marc Dymetman
5. Germán Kruszewski
6. Yuntian Deng
7. Anton Bakhtin
8. Dongyoung Go
9. Myle Ott
10. Jean-Marc Andreoli

Papers from Tetiana Parshakova's perspective

Ordered by how strongly the researchers Tetiana Parshakova 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. 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Global Autoregressive Models for Data-Efficient Sequence Learning (2019) — 23 citations
• Distributional Reinforcement Learning for Energy-Based Sequential Models (2019) — 23 citations