Nikolay Pavlovich Laptev

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 4 papers; trusts 38 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #9,894 of 35,345 by trust here, #13,962 by citations
• Probabilistic & Bayesian Methods — 10 citations into it
• Signal Processing — 9 citations into it; #1,769 of 7,572 by trust here, #2,114 by citations
• Statistics and Probability — 8 citations into it
• Economics and Econometrics — 7 citations into it; #12,494 of 23,481 by trust here, #17,346 by citations
• Computer Vision and Pattern Recognition — 6 citations into it
• Safety, Robustness & Interpretability — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Nikolay Pavlovich Laptev trusts overall

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

1. Yarin Gal
2. Rob J Hyndman
3. Simon S. Haykin
4. Thomas Opitz
5. Michael R. Chernick
6. Yoav Freund
7. Geoffrey E. Hinton
8. Zoubin Ghahramani
9. Eamonn J. Keogh
10. Diederik P. Kingma

Who trusts Nikolay Pavlovich Laptev overall

1. Alexander Lavin
2. Subutai Ahmad
3. Konstantinos Benidis
4. Lingxue Zhu
5. Bryan Lim
6. Mohsin Munir
7. Syama Sundar Rangapuram
8. Yijun Xiao
9. Gerald Woo
10. Qi Zhang

Papers from Nikolay Pavlovich Laptev's perspective

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

Their papers

• Large-Scale Unusual Time Series Detection (2015) — 203 citations
• Deep and Confident Prediction for Time Series at Uber (2017) — 373 citations
• Time-series Extreme Event Forecasting with Neural Networks at Uber (2017)
• Generic and Scalable Framework for Automated Time-series Anomaly Detection (2015) — 482 citations