Sergey I. Nikolenko

Computer Vision and Pattern Recognition · J. Comput. Biol. · 2012–2019

A researcher in the Science corpus: 14 papers; trusts 2,754 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 417 citations into it; #9,911 of 21,575 by trust here, #12,992 by citations
• Artificial Intelligence — 370 citations into it; #15,556 of 35,345 by trust here, #13,962 by citations
• Natural Language Processing — 97 citations into it
• Generative Models — 73 citations into it; #928 of 1,956 by trust here, #1,843 by citations
• Molecular Biology — 72 citations into it; #9,205 of 17,773 by trust here, #3,449 by citations
• Computational Theory and Mathematics — 67 citations into it
• Evaluation & Benchmarks — 60 citations into it
• Representation & Self-Supervised Learning — 57 citations into it

Whom Sergey I. Nikolenko trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Ross B. Girshick
6. Yoshua Bengio
7. Ilya Sutskever
8. Yann LeCun
9. Karen Simonyan
10. Andrew Zisserman

Who trusts Sergey I. Nikolenko overall

1. Xiu-Shen Wei
2. Sergey Nurk
3. Mekhail Mustak
4. Andrey D. Prjibelski
5. Dmitry Meleshko
6. Mike Roberts
7. Yi-Zhe Song
8. Dmitry Antipov
9. Tânia F Cova
10. Anton I. Korobeynikov

Papers from Sergey I. Nikolenko's perspective

Ordered by how strongly the researchers Sergey I. Nikolenko 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The Inclusive Images Competition (2019) — 19 citations
• Synthetic Data for Deep Learning (2019) — 471 citations
• Large-Scale Transfer Learning for Natural Language Generation (2019) — 93 citations
• Lost in Conversation: A Conversational Agent Based on the Transformer and Transfer Learning (2019) — 13 citations
• SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing (2012) — 23,317 citations
• Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (2018) — 910 citations
• BayesHammer: Bayesian clustering for error correction in single-cell sequencing (2012) — 447 citations
• druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. (2017) — 520 citations
• Breast Tumor Cellularity Assessment using Deep Neural Networks (2019) — 24 citations
• Adapting Convolutional Neural Networks for Geographical Domain Shift (2019) — 3 citations
• Tooth detection and numbering in panoramic radiographs using convolutional neural networks. (2019) — 407 citations
• Free-Lunch Saliency via Attention in Atari Agents (2019) — 28 citations
• Label Denoising with Large Ensembles of Heterogeneous Neural Networks (2018) — 30 citations
• Topic modelling for qualitative studies (2017) — 314 citations