Anastasia Borovykh

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 127 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #27,858 of 35,345 by trust here, #33,294 by citations
• Computer Vision and Pattern Recognition — 22 citations into it
• Model Architectures — 19 citations into it; #783 of 2,278 by trust here, #1,601 by citations
• Management Science and Operations Research — 15 citations into it; #2,195 of 11,946 by trust here, #3,705 by citations
• Signal Processing — 14 citations into it; #1,579 of 7,572 by trust here, #2,816 by citations
• Optimization & Training — 9 citations into it
• Generative Models — 8 citations into it
• Economics and Econometrics — 6 citations into it; #5,640 of 23,481 by trust here, #10,872 by citations

Whom Anastasia Borovykh trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Sepp Hochreiter
4. Yoshua Bengio
5. G. Zhang
6. Yann LeCun
7. Geoffrey E. Hinton
8. Aäron van den Oord
9. Alex Krizhevsky
10. Fisher Yu

Who trusts Anastasia Borovykh overall

1. Xiang Gao
2. Rajat Sen
3. Bryan Lim
4. Pedro Lara-Benítez
5. Omer Berat Sezer
6. Shaojie Bai
7. Alberto Gasparin
8. Saeed Khaki
9. Mikolaj Binkowski
10. Rahim Barzegar

Papers from Anastasia Borovykh's perspective

Ordered by how strongly the researchers Anastasia Borovykh 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Conditional Time Series Forecasting with Convolutional Neural Networks (2017) — 591 citations
• Dilated convolutional neural networks for time series forecasting (2018) — 117 citations